RM Secondary Report IQRA

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IQRA UNIVERSITY

The impact of education

expenditure on economicgrowth

SYED ASFAR ALI KAZMI (4807)

This study examines the eects of Educaon Expenditure protability on Economics Growth through

the me series data of Pakistan.


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The Impact of Education Expenditures on Economic Growth Time series Evidence from Pakistan.

5/18/2013

Abstract

This study examines the effects of Education Expenditure & Human Capital profitability on

Economics Growth. The education plays a vital role in enhancing economic growth by

increasing productivity. It is one of the important elements of human capital formation. The study

aims to examine the impact of education on economic growth of Pakistan based on an

econometric model. To test the relationship between educational expenditure and economic

growth, time series data has been used for the period of 1970-2010 for econometric analysis. The

empirical results reveal that there is no relationship between the two factors in short-run.

However, in long run a combination of several factors, including Education contribute towards

economic growth. The results have been tested for heteroscedasticity, multicollinearity and

autocorrelation for validation purposes. The study may be useful for educational sector for

policy making and human capital formation to augment economic growth in Pakistan.

Keywords: Educational expenditure, Gross fixed capital formation, economic growth, grossdomestic product, human capital.


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1. Introduction:

Education is a key to the socio-economic development of a country. It plays a vital role

in building human capabilities and accelerates economic growth through knowledge, skills and creative

strength of a society. The positive outcomes of education include reduction in poverty and inequality,

improvement in health status and good government in implementation of socio -economic policies.

It takes little analysis to see that education levels differ dramatically between developing and developed

countries. Building upon several decades of thought about human capital and centuries of general

attention to education in the more advanced countries it is natural to believe that a productive

development strategy would be to raise the schooling levels of the population. And, indeed, this is exactly

the approach of the Education for All initiative and a central element of the millennium development

goals.

We have concluded the result from the 10 research based on articles of the different countries.

Mostly results shows that there is a significant positive relationship in between education and GDP of the

country. The statisticalhypothesis testsproved it, that education impact direct proportional on GDP. When

the level of education increase, GDP will also be increase on the same direction.

For over two decades, the Pakistani economy has been growing on average at the very respectable rate of

about 5 percent per year, although this rate of growth has been comparable to that of other low- and

middle-income countries. But unfortunately it has been significantly below the growth rates experienced

by countries in South Asia such as Malaysia, Singapore, and Thailand. Illustration thought Figure 1

Economic Growth and Education.

Figure 1 Economic Growth and Educaon.


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This figure shows that development of per capita GDP over the time period 1970 2004 in a sample low

and middle income countries of South Asia. In the past few years, Pakistani growth has been accelerated;

it has started catch up with the courtiers in Southeast Asia.

Pakistan has been low compared to other South Asian and Southeast Asian countries. And while these

numbers are a little dated, the overall picture is unlikely to have changed much in recent years. Since

there is evidence in these literatures of a link between human capital and economic growth, this would

imply that investing more in human capital could help Pakistan maintain the high rates of economic

growth that is has recently been experiencing. Indeed, with growth accelerating, businessmen increasingly

list a shortage of skilled labor as a constraint to further expansion. Policy -makers in Pakistan recognize

this constraint and accordingly have attached great importance to strengthening education.

The link between investing in human capital and economic growth matters for an additional reason. A

large part of the worlds population continues to live in poverty, and the focus of economic researchers

and policy-makers has increasingly shifted toward designing policies that benefit the poor. There is

widespread agreement that economic growth is necessary to help reduce poverty, but that growth by itself

is not sufficient. Pakistan is a good example of this, as despite the relatively high growth rates, its social

development is weak and poverty remains widespread, with about an estimated 30 percent of thepopulation living in poverty. Investing in human capital, by creating a more productive work force, will

lead to higher future growth and incomes. And higher social spending on education and health care can

also benefit the poor directly by improving their current living conditions, as well as their future

prospects.


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The paper is structured as follow. Section II will review selectively the recent literatures on economic

growth, including findings regarding the importance of the quality of human capital. Following this,

Section III presents the results of an econometric analysis of growth in a large group of low- and middle-

income countries time series data during 19802005, adding a number of education and health indicators

to more conventional factors explaining growth, such as macroeconomic policies, initial income levels,

and institutional quality. In Further describes how Pakistan performed relative to the overall sample and to

countries in South Asia and Southeast Asia in particular. Based on these results, the concluding section

will offer some suggestions as to how Pakistan could maintain higher rates of economic growth into the

future.

2. Review of Literature:

Studies confirming a positive relationship between education expenditure and economic growth have

been made by Jorgenson and Fraumeni (1992), Aziz, Khan and Aziz (2008), Jung and Thorbecka (2001)

and Ogujiuba and Adeniyi (2005). In another finding, Lin (2004) found that higher education played a

strong role in Taiwan's economic growth (1% rise in higher education led to 0.35% rise in industrial

output and 0.15% rise in agricultural output.).

2.1Theoretical Background:

It is often proposed that expenditures on education should also be classified as gross fixed capital as a

form of investment in human capital. The acquisition of knowledge, skills and qualifications increases the

productive potential of the individuals concerned and is a source of future economic benefit to them

Education has always been observed as an imperative aspect in achieving the common aim of society. It is

clear in probing earlier growth theories, formations are evolutionary in nature, all relied on the basic

observations of human beings and the market place, formalized by analyzing historical data using

econometric modeling later on.

Beginning with Adam Smith (1776) opening sentence in The Wealth of Nations, Introduction and Plan,

proved to be significant of his whole position: The annual labor of every nation is the fund which

originally supplies it with all the necessities and conveniences of life which it annually consumes. Thus,

Adam Smith saw the source of all wealth in labor He saw society on its economic margin working


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automatically through rivalry and self-interest, the whole being unite together by division of labor and the

multiplex process of commute resulting there from.

In Schultzs work of 1960, 1961, & 1962 was based on the earlier endogenous growth theories, for

responding to the neoclassical growth theories .starting the work , highlighting the role of investing in

man as a medium to increase whole factor productivity.

The middle theories of growth emphasize at least three mechanisms through which education may affect

economic growth. First, education can increase the human capital in the labor force that is the way to

increases productivity and consequently transitional growth toward higher equilibrium level of output

neoclassical growth theories, Mankiw et al. (1992)

Second, education can the way to become greater or larger the containing power of the economy

innovatively, and the new knowledge on new technologies, products, and processes promotes

development as in theories of endogenous growth e.g., Lucas (1988), Romer (1990), Aghion and Howitt

(1998).

Third, education can facilitate the circulation and transference of knowledge needed to make progress

new intelligence and to successfully implement new technologies devised by others, which again

promotes economic growth.

The new growth theories point to endogenize technical advancement by adding something to the same

effect for a macro coming, emphasizing education as well as learning and R& for instance, the upper the

extent of education of the work force the upper the general productivity of capital as a result of the

additional educated area unit additional possible to initiate, and so have an effect on everyones

productivity, for example, according to Lucas (1998) externality is created as the increased education of

individuals raises not only their own productivity but also that of others with whom they interact so that

total productivity increases as the average level of education rises is shown in alternative models(Perotti,

1993)

The impact of education on the nature and growth of exports, which, in turn, affect the aggregate growth

rate, is differently human development influences macro performance.. The education and skills of a

developing countrys labor force predominance the creation of its broker boon and consequently

the masterpiece of its trade. And literacy, numeracy, and discipline, it has been argued that even

unskilled workers in a recent factory normally need these, which are no inheritable in first and lower

secondary seminary (Wood, 1994).


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2.2Empirical Studies:

(Afzal, Malik, Begum, Sarwar, & Fatima, 2011) Examined The relationship among Education, Poverty

and Economic Growth in Pakistan by using time series on education, real gross domestic product,

poverty, and physical capital for the time span from year1971 to 2010. Education, poverty, and physical

capital variables were considered to analysis impact on GDP of Pakistan. ARDL approach to co -

integration and TYAGC technique has been used. The result shows that there is significant relationship

exists among education, poverty physical capital and economic growth in long run. Better education can

be an effective tool for reducing poverty and enhance growth in country. Investing in education is the key

to develop economic growth of any country. On the basis of the findings of the study, the researchers were

recommended that the government and other policy makers should focus on short run as well as long run

solutions of poverty reduction. The study also recommends growth in Pakistan must be relying into

education enhancement and poverty reduction activities. Growth and education that generates income and

employment for the poor of the country can be critical for poverty reduction. Poverty reduction and

education enhancing strategies must be adopted to accelerate economic growth of the country.

(Kakar, Khilji, & Khan, 2011) Investigates The long-run relation between education expenditures and

economic growth in Pakistan by using time series data from year 1980 to 2009. Capital stock, labor

force, and human capital variables were considered to analysis impact on GDP of Pakistan. The two

techniques had been co-integration and error correction model. The result shows that capital stock and

labor force participation in economic growth of the country as few key variables that seem to effect the

economic development of Pakistan along with education in the long-run. The results confirm that

education has a long run relationship of economic growth. Better standards of education improve the

efficiency and productivity of labor force and effect the economic development in the long -run. Although

further, finding suggest that education quality is essential to increase the economic growth and human

capital abilities for the country, the government with competent administration at the lower level, should

increase the expenditure on education sector to promote research and development activities and improve

the quality of education in order to improve the economy's growth performance.

(Afzal, Farooq, Ahmed, Begum, & Quddus, 2010) Analysis The relationship between Education andEconomic Growth in Pakistan by using time series data from the period of 1970 to 2009. Physical

Capital, Poverty and Inflation variable is considered to examine the relationship of school education on

economic growth. Auto Regressive Distributive Lag (ARDL) approach to co-integration technique were

used in the study. Result shows that there is positive and significant relationship of physical capital, Net

School Enrollment Ratio on economic Growth in short run and long run as well. It is negative and


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significant relation of inflation on economic growth while surprisingly the long run impact of poverty on

school education is positive and significant and It is negative and significant in short run.

(Islam, Wadud, & Tariq Islam, 2007)Examined The causal relationship between education and income

(GDP) growth for Bangladesh by using annual time series data from year 1976 to 2003.Capital, labor,

and education variables were considered to analysis impact on GDP (dependent). Multivariate approach

Augmented Dickey-Fuller Unit Root test, Co-integration test and Granger causality test technique has

been used. The result shows that there is a significant and direct proportional relationship in

betweenGDP (dependent) to education. It appears that Bangladesh is in the second stage where income

and education are helping each other to grow.

(Abdul Latif & Mohamed Yusof, 2007)

A Latif & M Yusof - 2007 investigated the relationship between education and economic growth in

Malaysia. In this paper, time series data for the period 1980 through 2005 for Malaysia will be utilized to

determine to what extent education played an important role in economic growth. They analyses the

relationship between independent educational variables and gross domestic product (GDP) (as the

dependent variable).Data collected were analyzed using standard co-integration analysis to determine the

relationship between the respective variables the study reveals intriguing results. The result of this paper

suggests that there exists a co-integrating relationship between education as measured by enrollments

rates in primary, secondary and higher education and the GDP per capita.

(Khorasgani, 2008) Examined The effect of higher education on Irans economic growth by using time

series data from year 1959 to 2005. Capital stock, labor force, and human capital variables were

considered to analysis impact on economic growth of Iran. An autoregressive distributed lag (ARDL)

model and co-integration technique has been used. The result shows that capital stock and labor force

participation in economic growth of the country as few key variables that seem to effect the economic

development of Pakistan along with education in the long-run. The results confirmed that higher

education overall had a positive and significant effect on economic growth in Iran. The education factor

plays a vital role to increase real output in GDP of the country. The hypothesis of this research shows

that higher education has had a positively effect on the growth of the Iranian economy.

( K. Renuka & Alicia N., 2011) Evaluate the investment on education to Sri Lankas economic growth

during the period 1959-2008. Education & economic growth are variables that should be considered.

Correlation techniques have been used. The impact of education is assessed through a quality adjusted


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human capital stock measure. The returns to investment in education are positive but significantly lower

than those found for other developing economies. The results indicate a need for an appropriate strategy

to allocate resources on education to improve its returns to the economy.

(Asghar, Awan, & Rehman, 2012) Investigated The role of human capital in terms of education and

health on the economic growth of Pakistan. In this paper, annual time series data has been used for the

period 1974 through 2009. They analyses the impact of human capital (independent) variable on

dependent variable gross domestic product (GDP). Unit Root Tests, Vector Error Correction Model, and

Co-integration techniques have been used. The results confirmed that a significant positive impact of

human capital on economic growth and also have a proved that direct positive relationship exist between

economic growth and both measures of human capital through co-integration test. The result of this paper

suggests that there important implications particularly for policy makers that for achieving rapid

economic growth, it is indispensable to give much emphasis to human capital.

A BESKAYA & et al.(2010) aims to investigate the relationship between per capita school enrolments and

per capita economic growth in Turkey over the period 1923-2007. Using the Autoregressive Distributed

Lag (ARDL) approach to co-integration, The results also suggest that high school enrolments Granger-

cause higher education enrolments in the short run. The variance decomposition and impulse

response analyses confirm the results of Granger causality tests. The study suggested that the

implementation of eight-year mandatory primary education and recently establishing new

universities in every single province in Turkey may positively contribute to the countrys long-run

economic growth.

(Reza & Valeecha, 2012) examine the impact of education on economic growth of Pakistan based

on an econometric model. To test the relationship between educational expenditure and economic

growth, time series data has been used for the period of 1981-2010, combination of several factors such

as Labor Force Participation Rate, Gross Fixed Capital Formation including Education expenditure as

independent variables and Real GDP as dependent variable are analyzed by using OLS technique .The

results reveal that there is no relationship between the two factors in short -run. However, in long

run a combination of several factors, including Education contribute towards economic growth.

They concluded that Government of Pakistan should encourage International Companies and Local

Investors to grow their business in Pakistan that will leads to the Employment Opportunities and

Economic growth of Pakistan. The study may be useful for educational sector for policy making

and human capital formation to augment economic growth in Pakistan.


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3. Modeling Framework:

In accordance with the empirical studies, the regression model equation for Gross Domestic

Product (GDP) is expressed as a function of gross fixed capital formation (CAP) and education

expenditure (EDUEXP). Following equation is used to examine the effect of public expenditures on

education and gross fixed capital formation by government on GDP.

GDP = + 1 (CAP) + 2 (EDUEXP) + e ------------------ (1)

Where:

Y = Real GDP

EDUEXP = Government Expenditure on Education % of GDP

CAP = Gross Fixed Capital Formation

e = Error Correction Term

The above equation represents (EDUEXP) government expenditures on education which has positive

relationship with GDP. And (CAP) represents gross fixed capital formation which has positiverelationship with GDP. All the data sets used in this study from 1970 to 2010 are taken from World Bank.

4. Estimation and Results:

To the extent of preliminary stationary analyses, the integration properties of the data are

checked by using unit root test. Because of the likely structural breaks in the series, unit roots were

performed using the Augmented Dickey Fuller (ADF) statistic. In the model there is a chance of trend

(non-stationary) existence, which may be arise from external shocks and other sources of structural

instability, and might have occurred in the country in the period under examination.

Unit root tests for stationary were performed on both levels (at level & 1st difference) for all variables to

be used in the model.


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1

From the Table.4.1, the test results confirm the acceptance of the null hypothesis of unit root

(whether or not trend is included in the regression), at level for each variable on the basis of the ADF

test.1 First differencing of all the variables yields rejection of the null hypothesis on unit root (whether or

not trend is included in the regression) for each variable.

Table 4.12

Stationarity Test Results

Note: The critical values for ADF with constant (C) at 1%, 5% and 10% level of significance.

Based on these test results, it is, therefore, concluded that all series are first difference

stationary [i.e.I (1)]. After doing stationary test we run the OLS by using the variables of capital

structure, profitability and earning volatility at level that is shown in the table 4.2.

2 See Appendix-A, Table-1, Page#26.

Variables ADF test statistics

I(0) I(1)

C C

GDP 4.62 -2.24CAP -0.96 -3.78

EEDUEXP -0.63 -3.91


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Table 4.23

Long Run Determinants of GDP

To determine the relationship of considered variables, regression technique is applied. Results of the test

are shown in Table 4.2. It is clear that there is a significant impact of EDUEXP and CAP on GDP. In the

previous articles it is clear that the relationship between GDP and EDUEXP is depending on the long run

relationship. The result of Adj. R indicates that the model is capturing 96% variation and the value of

D.W is 0.797631 so, there was a chance of autocorrelation and we can check this through Breusch-

Godfrey Serial Correlation LM Test,and accepted our null hypothesis that is there is no autocorrelation is

present in our model . The combination of one or more of these series may exhibit a long run relationship.

We, confirm this through co-integration test. While the Engle-Granger single equation based co-

integration test have been used frequently in the literature, it has its shortcomings.

The most important is that when there are more than two variables in the model, there can be more than

one co-integrating vector.

Variable Coefficient t-Statistic Prob.

C 1.70E+09 0.786121 0.4367

CAP 2.082701 2.462535 0.0184

EDUEXP 30.73277 3.963139 0.0003

Adj. R 0.965075 F-statistic 553.6562

D.W 0.797631 Prob. 0.0000


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1

Table 4.34

Co-integration Test Results

We can check consistency of data through CUSUM and CUSUM of Squares test as

shown in the graph 4.4.

Graph 4.4

CUSUM and CUSUM of Squares test

4 See Appendix-A, Table-12, Page#36-37.

Hypothesized Trace 5%

Max.

Eigen 5%

No. of CE(s) Statistic

Critical

Value

value

statistics

Critical

Value

None * 65.66161 35.01090 34.77648 24.252

At most 1 30.88513 18.39771 23.77661 17.14769

At most 2 7.108517 3.841466 7.108517 3.841466

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1975 1980 1985 1990 1995 2000 2005

CUSUM of Squares 5% Significance

-20

-15

-10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000 2005 2010

CUSUM 5% Signi ficance


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1

In CUSUM test the results within 2 standard deviations but CUSUM of Squares test

shows fluctuation in 1981 till 2009 and outsides the 2 standard deviations.so, we can confirm this

through chow breakpoint test year take 1998 as shown in the table 4.5.

Table 4.55

Chow breakpoint

Prob.F(3,25) 0.0274

F-statistics 3.429096

In table 4.5 we can check the consistency of beta through Chow breakpoint test by

taking the year 1998 and the prob. value that is less than 0.1which means that there is a change in

coefficient before and after 1998 and we can reject our hypothesis. After inserting effect of

Nuclear Test in 1998.

Table 4.66

OLS Test of LOG Variables

Variables Coefficient t-Statistic Prob.C 4.4647424 8.528080 0.000

LOG(CAP) 0.674704 6.318972 0.000

LOG(EDUEXP) 0.229716 2.314363 0.026

Prob.F(3,24) 0.000 R 0.987

5. Causality Analysis:

The direction of causality between GDP, gross fixed capital formation and education

expenditure volatility remain unspecified. One mode of dealing with such an issue is to find out

5 See Appendix-A, Test , Page# 28.6 See Appendix-A, Test , Page#30.


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the direction of causality using Granger causality method. The usual Granger causality leads to

spurious regression results unless the variables in level are co-integrated. Also Granger causality

deals with bivariate regression model.

Table 5.17

Causality Test Result

The results of Granger causality test based on Toda and Yamamoto procedure are

reported in Table 5.1. The values in parentheses are probability values while rests of the

estimates are F-statistics. We accept our hypothesis: GDP does not Granger Cause CAP because

of the prob. value that is 0.2719 and as well as, we accept our hypothesis CAP does not Granger

Cause GDP because of the prob. value that is 0.4453. (No-Causality)

7 See Appendix-A, Table-14, Page#32.

Dependent

Variables GDP CAP EDUEXP

GDP - 1.24383 7.57276

- (0.2719) (0.0091)

CAP 0.59515 - 12.3888

(0.4453) - (0.0012)

EDUEXP 5.67604 2.26311 -

(0.0224) (0.1410) -


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1

We reject our hypothesis: GDP does not Granger Cause EDUEXP because of the prob.

value that is 0.0091 and we also reject our hypothesis EDUEXP does not Granger Cause GDP

because of the prob. value that is 0.0224. (bi-directional causality)

We accept our hypothesis: EDUEXP does not Granger Cause CAP because of the prob value that

is 0.1410, but we reject our hypothesis CAP does not Granger Cause EDUEXP because of the

prob.value that is 0.0012. (Uni-directional causality)

6. Conclusion and Implications:

Investing in education is the key to economic growth process. Education helps in reducing

poverty and improving the socio-economic status of both the individuals as well as the society.

The present research work explores the short-run (SR), long-run (LR) linkages and causal nexus

among education, poverty and economic growth in the presence of physical capital as a fourth

important variable.

The SR and LR relationship among variables has been examined through Bounds Testing

Approach to Co-integration and causality is tested though Toda-Yamamoto Augmented Granger

Causality (TYAGC) approaches. The co-integration results confirm that there exist LR

relationship among education, poverty, physical capital and economic growth, when each of the

economic growth, education and poverty serves as the dependent variable. Both the SR and LR

effect of PC on RGDP has been found to be positive and significant. EDUEXP affects GDP

positively and significantly only in the LR. Better education can be an effective tool for reducing

poverty and enhancing economic growth in Pakistan. The success of poverty reduction depends


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upon economic growth of the country as well as the manner in which the income of the country

is distributed.

The CUSUM and CUSUM Square tests confirm that the model is statistically stable and no

structural break found in model. The results of Toda Yamamoto Augmented Granger Causality

Tests confirm the bidirectional causality between education and economic growth. Physical

capital is causing each of the economic growth, poverty and education. The effect of education is

more on economic growth rather than the effect of Capital Formation on economic growth.

Physical capital seems to a very helpful variable in explaining the education, economic growth

and poverty linkages.

On the basis of the findings of the study, it is recommended that the government and other

Growth in Pakistan must be translated into education enhancement and poverty reduction

activities. Growth and education that generates income and employment for the poor of the

country can be critical for poverty reduction. Government should also focus on the quantity and

quality of education that, in turn, leads to more researches in the country. It is also recommended

that the linkages among education, capital formation and economic growth may further be

explored and generalized by including other macroeconomic variables other than physical

capital. Poverty reduction and education enhancing strategies must be adopted to accelerate

economic growth of the country


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1

Appendix A

1. OLS Table:

Dependent Variable: GDP

Method: Least Squares

Date: 05/06/13 Time: 03:25

Sample: 1970 2010

Included observations: 41

Variable Coefficient Std. Error t-Statistic Prob.

C 1.70E+09 2.16E+09 0.786121 0.4367

CAP 2.082701 0.845755 2.462535 0.0184

EDUEXP 30.73277 7.754655 3.963139 0.0003

R-squared 0.966821 Mean dependent var 5.54E+10

Adjusted R-squared 0.965075 S.D. dependent var 4.54E+10

S.E. of regression 8.48E+09 Akaike info criterion 48.63005

Sum squared resid 2.73E+21 Schwarz criterion 48.75543

Log likelihood -993.9160 Hannan-Quinn criter. 48.67571

F-statistic 553.6562 Durbin-Watson stat 0.797631

Prob(F-statistic) 0.000000

Regression model with log:

Dependent Variable: LOG(GDP)


Date: 05/06/13 Time: 17:12

Sample: 1970 2010



C 23.37131 0.086664 269.6758 0.0000

CAP -6.61E-11 3.40E-11 -1.946256 0.0590

EDUEXP 1.49E-09 3.11E-10 4.792460 0.0000

R-squared 0.857465 Mean dependent var 24.39798

Adjusted R-squared 0.849963 S.D. dependent var 0.878957S.E. of regression 0.340461 Akaike info criterion 0.753322

Sum squared resid 4.404715 Schwarz criterion 0.878705





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Dependent Variable: LOG(GDP)


Date: 05/15/13 Time: 06:06

Sample: 1970 2010



C 4.467424 0.523849 8.528080 0.0000

LOG(CAP) 0.674704 0.106774 6.318972 0.0000

LOG(EDUEXP) 0.229716 0.099257 2.314363 0.0261


Adjusted R-squared 0.986370 S.D. dependent var 0.878957

S.E. of regression 0.102616 Akaike info criterion -1.645293

Sum squared resid 0.400140 Schwarz criterion -1.519910

Log likelihood 36.72852 Hannan-Quinn criter. -1.599636



2. Auto Correlation Table:

Actual Fied Residual Table


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Serial Correlaonal LM Test:

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 63.59596 Prob. F(1,37) 0.0000

Obs*R-squared 25.91987 Prob. Chi-Square(1) 0.0000

Test Equation:

Dependent Variable: RESID


Date: 05/06/13 Time: 19:31

Sample: 1970 2010


Presample missing value lagged residuals set to zero.


C 0.027788 0.053379 0.520588 0.6058

CAP -1.57E-11 2.10E-11 -0.750382 0.4578

EDUEXP 1.07E-10 1.92E-10 0.557093 0.5808

RESID(-1) 0.805829 0.101048 7.974708 0.0000

R-squared 0.632192 Mean dependent var 2.49E-16








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2

Removals of Auto correlation:

Cochrane Orcutt Table

ER RE(-1)

Dependent Variable: ER


Date: 05/06/13 Time: 19:48

Sample (adjusted): 1971 2010

Included observations: 40 after adjustments


ER(-1) 0.787708 0.094336 8.349996 0.0000



S.E. of regression 0.197587 Akaike info criterion -0.380594Sum squared resid 1.522584 Schwarz criterion -0.338372


Durbin-Watson stat 1.500899

From the above table we take the value of coecient (row) =0.787708. The Prob. Value = 0.0000 is also

signicant. Thought this row value = 0.787708. We will generate new variables for our model.

Details following below:

1. TLGDP=LGDP-(0.787708)*LGDP(-1)

2. TCAP=CAP-(0.787708)*CAP(-1)

3. TEDUEXP=EDUEXP-(0.787708)*EDUEXP(-1)

Transpose

Dependent Variable: TLGDP


Date: 05/06/13 Time: 20:01




C 5.080043 0.035181 144.3989 0.0000TCAP 2.49E-11 2.30E-11 1.085246 0.2848

TEDUEXP 3.42E-10 2.22E-10 1.536127 0.1330








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2


LM Test for 1st

Transpose of Data




Test Equation:



Date: 05/06/13 Time: 20:06

Sample: 1971 2010

Included observations: 40Presample missing value lagged residuals set to zero.


C 0.010949 0.031630 0.346150 0.7312

TCAP -9.79E-12 2.08E-11 -0.471404 0.6402

TEDUEXP 5.78E-11 2.00E-10 0.289813 0.7736

RESID(-1) 0.504461 0.157098 3.211120 0.0028

R-squared 0.222652 Mean dependent var 1.05E-15




Log likelihood 25.78676 Hannan-Quinn criter. -1.028274F-statistic 3.437097 Durbin-Watson stat 1.839955



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2nd

Transpose of All Variables

Dependent Variable: RES


Date: 05/06/13 Time: 20:09




RES(-1) 0.485958 0.147725 3.289621 0.0022






Durbin-Watson stat 1.877200

1. TTLGDP=TLGDP-(0.485958)*TLGDP(-1)

2. TTCAP=TCAP-(0.485958)*TCAP(-1)

3. TTEDUEXP=TEDUEXP-(0.485958)*TEDUEXP(-1)

Dependent Variable: TTLGDP


Date: 05/06/13 Time: 20:14




C 2.648746 0.025902 102.2623 0.0000

TTCAP 3.45E-11 1.89E-11 1.830743 0.0754

TTEDUEXP 7.18E-11 1.87E-10 0.384867 0.7026









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LM Test of 2nd

Transpose of Data


F-statistic 0.211703 Prob. F(1,35) 0.6483Obs*R-squared 0.234480 Prob. Chi-Square(1) 0.6282

Test Equation:



Date: 05/06/13 Time: 20:17

Sample: 1972 2010


Presample missing value lagged residuals set to zero.


C 0.001031 0.026286 0.039235 0.9689

TTCAP -1.41E-12 1.93E-11 -0.072862 0.9423

TTEDUEXP 7.27E-12 1.89E-10 0.038412 0.9696

RESID(-1) 0.080161 0.174220 0.460112 0.6483

R-squared 0.006012 Mean dependent var -6.50E-16

Adjusted R-squared -0.079187 S.D. dependent var 0.121944







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3. Heteroskedasticity Test:

Heteroskedasticity Test: White



Scaled explained SS 10.88709 Prob. Chi-Square(5) 0.0537

Test Equation:

Dependent Variable: RESID^2


Date: 05/06/13 Time: 21:50

Sample: 1970 2010



C 0.210436 0.054435 3.865802 0.0005

CAP 1.29E-11 5.39E-11 0.239844 0.8119

CAP^2 -5.59E-21 7.64E-21 -0.731064 0.4696

CAP*EDUEXP 9.47E-20 1.50E-19 0.629349 0.5332

EDUEXP -3.25E-10 4.61E-10 -0.705478 0.4852

EDUEXP^2 -3.27E-19 7.49E-19 -0.436131 0.6654




Sum squared resid 0.730037 Schwarz criterion -0.646905Log likelihood 24.40227 Hannan-Quinn criter. -0.806356



Chow Breakpoint Test:

Chow Breakpoint Test: 1998

Null Hypothesis: No breaks at specified breakpoints

Varying regressors: All equation variablesEquation Sample: 1970 2010


Log likelihood ratio 10.56481 Prob. Chi-Square(3) 0.0143

Wald Statistic 10.28729 Prob. Chi-Square(3) 0.0163


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CUSUM graph

CUSM of SQUARES graph:

20

15

10

-5

0

5

10

15

20

1975 1980 1985 1990 1995 2000 2005 2010

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1975 1980 1985 1990 1995 2000 2005 2010

CUSUM of Squares 5% Significance


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2

4. Stationary and Non-Stationary:

At Level Graph (Trend)


28/40


2

At 1ST

difference graph (trend)


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2

ADF TEST AT LEVEL FOR GDP

Null Hypothesis: GDP has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on AIC, maxlag=1)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic 4.620441 1.0000

Test critical values: 1% level -3.605593

5% level -2.936942

10% level -2.606857

*MacKinnon (1996) one-sided p-values.

ADF TEST AT 1ST

DIFFERENCE FOR GDP

Null Hypothesis: D(GDP) has a unit rootExogenous: Constant


t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -2.240239 0.1961


5% level -2.941145

10% level -2.609066



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2

ADF TEST AT LEVEL FOR EDUEXP

Null Hypothesis: EDUEXP has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.938987

10% level -2.607932


ADF TEST AT 1ST

DIFFERENCE FOR EDUEXP

Null Hypothesis: D(EDUEXP) has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.938987

10% level -2.607932


ADF TEST AT LEVEL FOR CAP.

Null Hypothesis: CAP has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.938987

10% level -2.607932



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ADF TEST AT 1ST

DIFFERENCE FOR CAP

Null Hypothesis: D(CAP) has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.938987

10% level -2.607932


LGDP = + 1 (cap) + 2 (eduexp) + e ------------------ (2)

5. Co-integration Test:

Date: 05/06/13 Time: 20:32

Sample: 1970 2010


Series: GDP CAP EDUEXP

Lags interval: 1 to 1

Selected(0.05 level*)Number of

CointegratingRelations by

Model

Data Trend: None None Linear Linear QuadraticTest Type No Intercept Intercept Intercept Intercept Intercept

No Trend No Trend No Trend Trend Trend

Trace 2 2 2 2 3

Max-Eig 2 2 2 2 3

*Critical values based on MacKinnon-Haug-Michelis (1999)

InformationCriteria by


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Rank andModel

Data Trend: None None Linear Linear Quadratic

Rank or No Intercept Intercept Intercept Intercept Intercept

No. of CEs No Trend No Trend No Trend Trend Trend

LogLikelihood byRank (rows)and Model(columns)

0 -2557.602 -2557.602 -2555.007 -2555.007 -2549.755

1 -2533.349 -2533.081 -2531.065 -2527.705 -2524.416

2 -2522.699 -2521.954 -2521.633 -2517.119 -2515.708

3 -2522.272 -2520.418 -2520.418 -2513.602 -2513.602

AkaikeInformationCriteria by

Rank (rows)

and Model(columns)

0 131.6206 131.6206 131.6414 131.6414 131.5259

1 130.6845 130.7221 130.7213 130.6002 130.5342

2 130.4461 130.5104 130.5453 130.4163 130.3953*

3 130.7319 130.7907 130.7907 130.5950 130.5950

SchwarzCriteria by

Rank (rows)and Model(columns)

0 132.0045 132.0045 132.1532 132.1532 132.1657

1 131.3244* 131.4046 131.4891 131.4107 131.4299

2 131.3419 131.4915 131.5690 131.5254 131.54703 131.8836 132.0703 132.0703 132.0026 132.0026

Co-integration table after seeing Option And Lag Value:

Date: 05/06/13 Time: 20:51



Trend assumption: Quadratic deterministic trend

Series: GDP CAP EDUEXP

Lags interval (in first differences): 1 to 2

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.599552 65.66161 35.01090 0.0000


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At most 1 * 0.465113 30.88513 18.39771 0.0005

At most 2 * 0.170611 7.108517 3.841466 0.0077

Trace test indicates 3 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.599552 34.77648 24.25202 0.0014

At most 1 * 0.465113 23.77661 17.14769 0.0047

At most 2 * 0.170611 7.108517 3.841466 0.0077

Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Dependent Variable: LGDP


Date: 05/06/13 Time: 23:17

Sample: 1970 2010



C 4.467424 0.523849 8.528080 0.0000LOG(CAP) 0.674704 0.106774 6.318972 0.0000

LOG(EDUEXP) 0.229716 0.099257 2.314363 0.0261









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3

6) Granger Causality Test:

Pairwise Granger Causality Tests

Date: 05/08/13 Time: 17:08Sample: 1970 2010

Lags: 1

Null Hypothesis: Obs F-Statistic Prob.

CAP does not Granger Cause GDP 40 0.59515 0.4453

GDP does not Granger Cause CAP 1.24383 0.2719

EDUEXP does not Granger Cause GDP 40 5.67604 0.0224

GDP does not Granger Cause EDUEXP 7.57276 0.0091

EDUEXP does not Granger Cause CAP 40 2.26311 0.1410

CAP does not Granger Cause EDUEXP 12.3888 0.0012


35/40


3

Pool or Panel Data:

Country Name Dependent Variable(DV) Independent Variable(IV) Independent Variable(IV)PAKISTAN GDP CAP EDUEXP

INDIA GDP CAP EDUEXP

BANGLADESH GDP CAP EDUEXP

SRI LANKA GDP CAP EDUEXP

Random Effect Model Test (REM):


Method: Panel EGLS (Two-way random effects)

Date: 05/08/13 Time: 01:03Sample: 1980 2010

Periods included: 31

Cross-sections included: 4

Total panel (balanced) observations: 124

Swamy and Arora estimator of component variances


C 1.53E+10 2.29E+09 6.652637 0.0000

CAP 1.443769 0.083864 17.21549 0.0000

EDUEXP 17.06292 0.823413 20.72220 0.0000

Effects Specification

S.D. Rho

Cross-section random 2.06E+09 0.0158

Period random 0.000000 0.0000

Idiosyncratic random 1.62E+10 0.9842

Weighted Statistics



S.E. of regression 1.85E+10 Sum squared resid 4.13E+22




36/40


3

Hausman Test:

Correlated Random Effects - Hausman Test

Equation: Untitled

Test cross-section and period random effects

Test SummaryChi-Sq.Statistic Chi-Sq. d.f. Prob.

Cross-section random 0.000000 2 1.0000

Period random 0.000000 2 1.0000

Cross-section and period random 19.030917 2 0.0001

* Cross-section test variance is invalid. Hausman statistic set to zero.

* Period test variance is invalid. Hausman statistic set to zero.

** WARNING: estimated period random effects variance is zero.

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

CAP 1.766465 1.443769 0.004047 0.0000

EDUEXP 13.035078 17.062921 0.784478 0.0000

Fixed Effect Model Test (FEM):


Method: Panel Least Squares

Date: 05/08/13 Time: 02:14

Sample: 1980 2010

Periods included: 31


Total panel (balanced) observations: 124


C 2.23E+10 2.55E+09 8.770649 0.0000

CAP 1.698747 0.126735 13.40388 0.0000

EDUEXP 13.43582 1.418477 9.472007 0.0000

Effects Specification

Cross-section fixed (dummy variables)

Period fixed (dummy variables)




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S.E. of regression 1.62E+10 Akaike info criterion 50.09439

Sum squared resid 2.31E+22 Schwarz criterion 50.91318




Unit Root Test for Panel Data:

1) GDP AT LEVEL:

Null Hypothesis: Unit root (individual unit root process)

Series: GDP

Date: 05/08/13 Time: 15:19

Sample: 1980 2010

Exogenous variables: Individual effects

User-specified lags: 1

Total (balanced) observations: 116


Method Statistic Prob.**

Im, Pesaran and Shin W-stat11.808

2 1.0000

** Probabilities are computed assuming asympotic normality

2) GDP AT 1st Difference:


Series: D(GDP)

Date: 05/08/13 Time: 15:26

Sample: 1980 2010






Im, Pesaran and Shin W-stat

1.1582

6 0.8766



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3) CAP AT LEVEL:


Series: CAPDate: 05/08/13 Time: 15:29

Sample: 1980 2010







2 1.0000


4) CAP AT 1st Difference:


Series: D(CAP)

Date: 05/08/13 Time: 15:30

Sample: 1980 2010






Im, Pesaran and Shin W-stat-

0.52650 0.2993


5) CAP AT 2ND Difference:


Series: D(CAP,2)

Date: 05/08/13 Time: 15:31

Sample: 1980 2010







9.26582 0.0000



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6) EDUEXP AT LEVEL:

7) EDUEXP AT 1st Difference:


Series: D(EDUEXP)

Date: 05/08/13 Time: 15:34

Sample: 1980 2010







2.29292 0.0109



Series: EDUEXP

Date: 05/08/13 Time: 15:34

Sample: 1980 2010







9 1.0000



40/40


7.References:

BibliographyK. Renuka, G., & Alicia N., R. (2011). The impact of education investment on Sri Lankan economic growth.

SriLanka: Pergamon.

Abdul Latif, N., & Mohamed Yusof, N. (2007). The relationship between education and economic growth

in Malaysia. Malaysia.

Afzal, M., Malik, M. E., Begum, I., Sarwar, K., & Fatima, H. (2011). Relationship among Education, Poverty

and Economic Growth in Pakistan: An Econometric Analysis. Lahore, Pakistan: Journal of

Elementary Education Vol.22, No. 1 pp.23-45.

Asghar, N., Awan, A., & Rehman, H. u. (2012). Human Capital and Economic Growth in Pakistan: A

Cointegration and Causality Analysis Vol. 4, No. 4; April 2012. Lahore, Pakistan: International

Journal of Economics and Finance.

BESKAYA, A., & et, &. a. (2010). THE IMPACT OD EDUCATION ON ECONOMIC GROWTH IN TURKEY.

Turkey: Suleyman Demirel Univeristy The Journal of Faculty of Economices and Administrative.

Islam, T. S., Wadud, M. A., & Tariq Islam, Q. B. (2007). "Relationship between education and GDP growth:

a mutivariate causality analysis for Bangladesh." Economics Bulletin, Vol. 3, No. 35 pp. 1-7.

Bangladesh.

Kakar, Z. K., Khilji, D. A., & Khan, M. J. (2011). Relationship between Education and Economic Growth in

Pakistan. Islamabad,Pakistan: Journal of International Academic Research (2011) Vol.11, No.1.

Khorasgani, M. F. (2008). "Higher education development and economic growth in Iran", Education,

Business and Society: Contemporary Middle Eastern Issues, Vol. 1 Iss: 3, pp.162 - 174. Isfahan,

Iran: Emerald Group Publishing Limited.

Reza, A., & Valeecha, S. (2012). Impact of Education on Economic Growth of PakistanEconometric

Analysis. Karachi,Pakistan: IOSR Journal of Business and Management (IOSR-JBM) ,ISSN: 2278-

487X. Volume 5, Issue 4 (Nov. - Dec. 2012), PP 20-27.

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