secondary report

7/28/2019 Secondary Report

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Intellectual Capital performance in the banking sector of Pakistan

1

Dated: 15th May 2013

RESEARCH METHODS

Topic

INTELLECTUAL CAPITAL PERFORMANCE IN THE BANKING SECTOR

Presented to the Faculty of the

Department of Management Sciences

In the fulfillment of the course (Research methods)

SUBMITTED TO

Sir Tehseen Jawaid

By

HIRA Ashraf (6458)

Hira Perveen (6632)

Madiha Sarfaraz (6224)

Syed Hasan Nemat (6243)


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Acknowledgement

We take this opportunity to express our profound gratitude and deep regards to our Teacher (Sir

Tehseen Jawaid) for his exemplary guidance, monitoring and constant encouragement

throughout the course. The blessing, help and guidance given by him time to time will carry us a

long way in the journey of life on which we are about to embark.


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Table of Content


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Abstract:

This study aims to examine the measures of intellectual capital performance of Pakistanicommercial banks for the period of 2006 2011. The required data was obtained from the

annual reports of Pakistani commercial banks. The intellectual capital performance is measured

by Value- added intellectual coefficient (VAICTM) method using ordinary least square techniques

(OLS) to test the relationship between intellectual capital performance as dependent variable

while Human capital and Capital employed as independent variables. The paper reveals that

there is a direct relationship between intellectual capital, human capital and capital employed.

All of these are associated with profitability of banks and its performance and discusses their

impact on banks value- based performance. However, this study confirms the vast difference

existed in the performance of Pakistani bank over the study period.

Key words: Intellectual capital, Human capital, Valued- added performance, VAICTM.

1. INTRODUCTION:

A growing and dynamic banking sector is essential for economic growth in

Pakistan as growth in banking sector reinforces the economic growth. The banking sector

constitute the core of the financial sector in Pakistan therefore to identify the effective growth of

banking sector one should know about its performance and value addition process, how much

improvement are needed and in which areas should be focused greatly to enhance the

performance.

Several studies are performed on the intellectual capital performance in relation with business

performance as it is considered as the key success factor for future development in any sector. In

most of the studies panel data1 is used also the studies based on VAICTM method2 that the

relationship between value-added, physical capital and human capital is significant. Firms spend

a huge amount annually to train their employees in business-specific topics thus increasing the

competency of the staff. This capital employed provides a high return to the company, thus

1 (Mavridis, Kyrmizoglou (2005), Kamath (2007) and Pucar (2012)

2 Pulic (2003 & 2005)


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increasing the worth of a business. IC performance measurement and management have become

more important when service sectors are playing a major role in economic growth around the

globe, thus can helpful in increasing the GDP. In case of inefficient functioning of financial

sector no economy can grow and improve the living standards of its Population.

Pakistans financial system has grown in recent years but continues to have an enormous growth

potential. The system remains relatively small in relation to the economy, when compared with

other emerging countries in Asia and around the world. Given that a dynamic and growing

financial system is central to a growing economy, the small size (lack of depth) of Pakistans

financial sector implies that many financing needs cannot be met and that much of the countrys

economic potential remains unfulfilled. Banks happen to be one service sector that uses a huge

amount of human capital and customer capital for its survival. Banks in Pakistan account for 95

percent of the financial sector and hence good health of banks is directly related to economic

growth and development of Pakistan.

This paper determines the banks efficiencies in utilizing their intellectual capitals, therefore a

contribution to the existing literature on Pakistani banks and intellectual capital as its objective is

to add a new dimension to measure and evaluate value-based performance efficiency of firms in

service sector, especially banks in Pakistan. This paper comprises of four parts: Part1

introduction; Part 2 literature review; Part 3 methodology; Part 4 estimation and result of

stationarity, regression analysis and cointegration test; Part 5 tests the direction of causalitybetween the variables of the model. Final part is conclusion of the study.

2. Literature Review:

The literature on the intellectual capital performance in the banking sector is older and clear and

there are well establish theories and models that can clearly identify this effect.

2.1 Theoretical Background:

In recent years the intellectual capital literature has exhibited relatively few theoretical

contributions, in contrast to the flurry of such work in the period 1996-2003. The greater part of

the existing theoretical corpus is found to have a normative quality, something particularly

evident in policy-oriented contributions on accounting for intellectual capital.


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Hugh McDonald, who worked for a British computer manufacturer, the concept was introduced

to the American business community in Stewart's3 1991. Between 1991 and 1997 intellectual

capital began receiving more and more attention from the business community, the business

media, and publishers of business books. In the book's first chapter Stewart clarifies the

extraordinary theory of intellectual capital via a rather mundane object the aluminum beer can.

Economist, Leif Edvinsson of Skandia4, one of Sweden's largest financial services firms,

measured intellectual capital as the difference between the market value of a corporation and the

book value of its equity. Edvinsson stated that the balance sheet is an outmoded way of

measuring a company's worth. He cited data showing that over the past dozen years or so (and

perhaps not coincidentally coinciding with the Information Age) the relationship between share

prices and reported equity value has been weakening.

Other methods that include intellectual capital in measuring a corporation's worth are the

"balanced scorecard" and Skandia Navigator. The balanced scorecard method 5 was introduced by

Robert S. Kaplan and David P. Norton in 1992 in an article in the Harvard Business Review.

This method attempts to measure knowledge and worth by scoring four categories:

Customer: customers need expectations, satisfactions, etc.

Internal processes: those processes that drive the company on a day-by-day basis, those

processes that best respond to the customer category, etc.

Innovation and learning: looking to the future to increase value.

Financial: the result of the first three categories, the extent to which the company is

creating value for shareholders, etc.

The Skandia Navigator6 as described by David J. Skyme and Debra M. Amidon in Journal of

Business Strategy views intellectual capital as the "hidden value of a corporation." The

3 Stewart, Thomas,Fortune, 3 June (1991)

4 Edvinsson, Leif, and Michael S. Malone New York: HarperBusiness, (1997)

5 Kaplan, Robert S., and David P. NortonHarvard Business Review(1992)


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Navigator is similar to the balanced scorecard and provides taxonomy of five categories:

financial, customer, human, process, and renewal.

2.2 Empirical Studies:

Cheng, Lin, Hsaio and W.Lin (2010) investigate invested resource, competitive intellectual and

corporate performance by using time- series data from 2002- 2005, variables are innovative

capacity, maintainable customer relationship, human value added, efficient operating process and

firms performance. Structural path model have been used. Results shows that firms can improve

their performance by improving innovative capacity and process reformation, thus it is followed

by value- added human capital. It is recommended that those managers who wish to produce

immediate improvement in corporate value should consider innovative capacity and efficient

operating processes. Future research can divide performance measure into two segments market

and financial performance and investigate their relationship.

Mauridis and kyrmizoglou (2005) investigate intellectual capital performance in the Greek

banking sector by using time series data from 1996- 1999 of the 17 biggest Greek banks,

variables are branches, employees, equity, Rev (out), Exp (in) Value- added (VA), Physical

capital (CA), Human capital (HC), structural capital (SC= VA-SC) . Regression analysis has

been used. Results shows that there is a strong positive correlation between value added and

physical capital but especially with HC, similarly same kind of relationship found between value

added and variable Out ,In, Gross Profit, braches, Employees and Equity. However the

relationship between best performance indicator (BPI) and above written variable have

insignificant, slight negative and weak correlation, so it is very clear that best performing banks

have shown a positive result of Using human capital (HC) and less in usage of physical capital

(CA). It is recommended that intellectual capitalist or knowledge workers provides a positive

impact on corporate success also its value addition.

Mavridis (2004) investigate the intellectual capital performance of the Japanese banking sector

by using cross- sectional date for the financial period 1st April 2000 31st March 2001. Variables

are value- added (dependent) while HC and CA is independent variables. Regression analysis has

been used. Results shows that that the VA variable have a normal, strong and significant positive

6 Skyrme, David J., and Debra M. Amidon (1998)


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relationship with CA but not with HC both of these helpful in BPI performance in different ways

as HC is highly contributing in BPI performance so it is concluded that BPI bank have less in

usage of CA while shown a positive result in the usage of HC. It is recommended that further

research should be continued with a longer time period (almost 50 years) to highlight the fact

that intellectual capital or knowledge workers provides a strong contribution to corporate

performance.

Ting and Lean (2009) investigate the intellectual capital performance of financial institution in

Malaysia by using time series data from 1999- 2007. Variables are ROA (dependent) and

independent variables are components of VAIC which includes Human capital efficiency (HCE),

Structural capital efficiency (SCE) and capital employed efficiency (CEE). To analyze the

performance they divide the financial institution into banks and non- banks institutions.Regression analysis has been used. Results show that that there is a significant positive

correlation found between VAIC components and ROA as HCE and CEE has a strong impact on

profitability while SCE has not. It I concluded that in order to maximize the firms profit, there

should a proper or maximum utilization of resources, therefore management can be able to

increase the potential of a firm Intellectual capital thus maximize the benefits of stake holders

too. It is recommended that future study should be on many public listed financial institutions,

which cover all companies in the sector, however also suggest that in order to examine the value

creation efficiency in financial institution for long-term objective of shareholders wealth.

Swartz and firer (2005) investigate the board structure and intellectual capital in South Africa by

using cross- sectional data of 2003. Dependent variable is VAIC and independent variables are

Perethnic and pergender. Multiple regression models are developed by using linear regression

analysis. The result shows that there is a positive correlation between the percentage of ethnic

member of companies Board of directors and IC performance, thus it is suggested that south

African public listed companies may be able to improve or to increase the IC performance by

using an ethnically diverse Board of directors therefore the company business has become

successfully progressed thus increase company performance. It is recommended that this study

further extended by considering nation with different corporate structure.

Stevo Pucar (2012) analyzed the impact of intellectual capital (IC) on export performance of

firms and industries by using financial statements of 134 countries of Bosnia and Herzegovina


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from 2004 to 2007. The intellectual capital as an independent variable and an export

performance as a dependent variable were measured as growth of exports. Value added

intellectual coefficient (VAIC) is one of the most often used methods of intellectual capital

measurement. The results show that influence of intellectual capital on export performance really

indicates competitive advantage of sectors within the economy of one country. It is

recommended that future research can also be done goal to determine which types of production

and products have the largest influence of the human and intellectual capital on the export

performance.

Sharabati, Jawad and Bontis (2010) was to empirically test the relationship between intellectual

capital (i.e. human capital, structural capital, relational capital) and business performance within

the pharmaceutical sector of Jordan by using data of 15 organizations that were registered in the

Jordanian Association of Pharmaceutical Manufacturers (JAPM) in 2007. A valid research

instrument was utilized to conduct a survey of 132 top- and middle-level managers from all 15

members of the Jordanian Association of Pharmaceutical Manufacturers. The results of this

study have shown that there is in fact strong and positive evidence that pharmaceutical firms in

Jordan are managing intellectual capital effectively and that in turn is influencing business

performance positively. It is recommended that data should be considered front-line employees

as well as from the boards of directors and other variables can also be considered.

Wang and Chang (2005) investigated the impact of intellectual capital elements on businessperformance, as well as the relationship among intellectual capital elements from a cause-effect

perspective in Taiwans IT industry, provides some implications for management in the IT

industry. The variables were used human capital, innovation capital, process capital, and

customer capital. The partial least squares approach is used to examine the information

technology (IT) industry in Taiwan. The results found that the cause-effect relationship between

elements of intellectual capital and business performance intellectual capital elements directly

affect business performance, with the exception of human capital. Human capital indirectly

affects.

Roslender and Fincham, (2004) discussed some of the findings of a recently completed field

study of intellectual capital accounting developments in the UK, funded by one of the

professional accountancy bodies through a series of interviews with senior managers in six

organizations conducted between May 2001 and March 2002. Reports from a number of


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Australian, Canadian and European enquiries have added to the momentum of the intellectual

capital accounting project, whilst affirming its links with contemporary debates about the

information society, intangibles, knowledge management and business reporting. Companies will

evidence little interest in intellectual capital, and that its presence is largely unsystematic in

nature and closely linked with the interests of individuals.

Tovstiga and Tulugurova (2007) investigated the impact of intellectual capital practices on

enterprise performance in small innovative enterprises (SIEs) in the St Petersburg, Russia region.

Research fieldwork used a survey questionnaire focusing on technology-intensive SIEs in the St

Petersburg region, supported by a select number of follow-up interviews. The research analysis

is based on quantitative statistical evaluation of the research data. The research reported in this

paper is limited by the relatively small sample size of firms surveyed; the quantitative research is

based on perceptions of managers. Intellectual capital, particularly structural and human capital,

is perceived by Russian managers of SIEs to be a primary determinant of enterprise performance,

thereby substantiating the importance of the resource-based view for enterprise performance

even in the transitional economy of Russia.

Yalama and Coskuni (2007) studied the intellectual capital performance of quoted banks on the

Istanbul Stock Exchange markets by using the time- series data for the period 1995-2004.

Variables are input and output variables which are tangible assets, intangible assets and financial

effects on profitability (ROA, ROE and LDR). VAIC method and DEA were performed by usingthe efficiency measurement system (EMS) software. The result shows that for banks physical

capital become less important than that of intellectual capital also intellectual capital is important

for investors too and it creates value addition by increasing productivity level thus it is necessary

for banks to efficiently manage their intangible assets. It is recommended that further research

shows all the banks.

Goh (2005) studied the intellectual capital performance of commercial banks in Malaysia by

using the time series data for the period 2001 2003. Variables are HCE, CEE and SCE, the sum

of these is VAIC. VAIC method is used. The results show that in order to create value in the

efficiency of Human capital, physical capital and structural capital, Intellectual capital acts as a

key resource factor. It is found that HCE plays a major role in value creation than that of

Physical and Structural capital thus investment in HCE results in an increase returns so it


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improves value creation capability and efficiency. It is recommended that for future study, one

should consider all of the banks in Malaysia before and after 2001.

Haji and Mubarak (2012) investigate trends of IC disclosure; evidence for the Nigerian banks

sector by using the time series data for the period of 2006 2009. Variable are Intellectual

capital, human capital and external capital. Different techniques are used like Friedman test,

Wilkinson signed ranks or sample t- test and Kreskas Walks test were performed. The results

show that with the passage of time IC disclosure of the banks significantly important at a

significant interval. However internal capital shows only intellectual capital disclosure shows

increasing trends. It is recommended that future studies examine the IC disclosure of the banks

and are also incorporate others companies having same regulatory challenges.

Kamath (2007) investigate the Intellectual capital performance of Indian banking sector by using

the time-series data for the year 2000 - -2004. Variables are Value added, physical capital and

human capital, two regression models are run, VA acts as dependent variables in both models

and capital employed as independent variables in one while HC is independent in others. The

results shows that foreign banks have good HCE that that of domestic banks therefore creating

more value addition while the domestic bank have not efficient workforce thus foreign banks are

highly efficient in performance than that of domestic banks. It is recommended that same study

can further be analyzed in the other sector and can be considered as a bench mark.

Caliser, Cigdem, Bayraktaroglu and Deniz (2010) investigates that how to apply Value Added

Intellectual Coefficient (VAIC) of public to compare quoted information technology &

communication companies on the Istanbul Stock Exchange (ISE) & it also has the VAIC & its

components impact on performance of the company by using the primary data from the year

2005-2007.Market valuation, Profitability, Productivity, Return on equity, Firm leverage, Firm

size, VAIC,HCE,CEE & SCE variables are considered. The Multiple regression analysis

technique was used. The result shows that the human capital efficiency had the positive effect on

both profitability & return on the equity. The firms leverage & size was also found to be

significant predictors of profitability & The human capital had the highest impact on

profitability. Human capital & capital employed efficiencies have the positive effect on the

return on equity. The market valuation has been explained by the firm size.


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Kamukama, Ahiauzu and Ntyani (2010) investigates the interaction effect of the intellectual

capital elements & how they fuse to affect financial performance in microfinance institutes &

to explore the appropriate blend or mix of the intellectual capital elements by using primary data

from the year 1995 to 2010. Structural capital, Human capital, Relational capital & Performance

variables are considered & the technique used is Regression Analysis. The result shows that the

interactive terms boosts the predictive power of the main effects to explain variance in financial

performance. The effects of human capital on financial performance depend on the different

level of relational capital. The positive & significant relationship exists between the Human

capital, Structural & relational capital & financial performance in micro finance industry & It is

recommended that the further research should be undertaken to examine the multiplicative

effects studied in the paper across the time.

Guthrie & Abhayawansa (2010) investigates to review & synthesis current knowledge on the

importance of intellectual capital information to the capital market by using primary data from

the year 1977 to 2001.The variables used are customers, external, human, internal,

organizational, R&D and strategy by using VPA technique. Result shows that IC is more

important to the analysts but there is much variation in the result. It is recommended that the

more research is possible to refine the current understanding of the importance of IC to the

capital market.

Bannany (2012) investigates the determinants of the intellectual capital performance of UAE

banks by using primary data from 2004 to 2010.VAIC, GFC, CR2DEP, FATA, LGIT, LGRESV,

LGASS, ROA, LGAGE & LGLSAG are the considered variables. Multiple regression analysis

has been used. The result shows that the model is significant & explains 78 percent of

relationship. There is significant relationship between the GFC & market structured .Its

recommended to do further research for corporate governance & financial leverage.

Mondal and Ghosh (2012) studied the relationship between Intellectual capital and financial

performance by using the time series data for the period 1999 2008. Variables are ROA, ROE,

VAIC, LTA, DE, HCE, SCE, CEE and ATO. Multiple regression analysis has been used. The

result shows that IC plays a major role in increasing profitability and productivity in the banking

sector. However, if human capital is efficient which is increasing the Banks return. However,

the roles and attributes of SC for promoting financial performance are subject to future research.


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In order to check that our regression results are not spurious we run the preliminary stationarity

analysis, but here we cant able to perform it because of insufficient number of observation. The

predictive analysis at that stage should confirm the existence of basic theoretical relationship7

which means that Value added (VA) primarily must be related with Human capital (HC) and

capital employed (CA). For this reason OLS regression is run under the level of significance

0.05. Result of the test is shown in Table 4.1. According to the article 8 there is a significant

impact of HC and CA on VA.

Table 4.19

Long-Run Determinants of Intellectual capital

Source: Authors Estimation

The result of Adj. R indicates that the model is capturing 88% variation and the value of D.W is

1.050209 so, there was a chance of autocorrelation and we can check this through Breusch-

Godfrey Serial Correlation LM Test10 and after this test we can remove the problem of

autocorrelation through GLS Cochrane-Orcutt 1949 model11.After applying this model we again

did the Breusch-Godfrey Serial Correlation LM Test 12 and accepted our null hypothesis that is

7Bontis (1998)

8 Mavridis, Kyrmizoglou(2005)

9 See Appendix, table # 1, Pg no. 24

10 See the prob value of Breusch-Godfrey Serial Correlation LM Test is 0.0000.11 See the D.W value is 2.015465.12 See the prob value of Breusch-Godfrey Serial Correlation LM Test is 0.325.

Variable Coefficient t-Statistics Prob.

C -1882696.-3.321734

0.0012

CA 0.308621 9.330818 0.0000

HC 0.697502 2.509936 0.0134

Adj. R2 0.879886 F.Stat. 436.8629

D.W. 1.050209 Prob. 0.000000


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there is no autocorrelation is present in our model . The test results of estimated equation after

removing autocorrelation are shown in Table 4.2.

Table 4.213

Long-run Determinants of Intellectual capital

Variables Coefficients t-Stats. Prob.

C -1.36E+14 -6.229449 0.0000

CA 7683635 6.045328 0.0000

HC40272893

3.771865 0.0003

Adj. R2 0.833776 F.Stats 296.9419

D.W. 2.015465 Prob. 0.00000


The long run coefficient of CA and HC have expected sign and highly significant. The

coefficient ofHC is fewer than coefficient of CA. This implies that human capital has more

effect than Capital Employed on Intellectual Capital Performance. To check the problem of

heteroscedasticity, White heteroskedasticity test has been applied. Test results suggest that

heteroscedasticity exist, for this we applied heteroskadeticity test (see Appendix A).

The combination of one or more of these series may exhibit a long run relationship. We, confirm

this through cointegration test. While the Engle-Granger single equation based cointegration 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 cointegrating

vector. The approach developed by Johansen (1988, 1991) and extended by Johansen and

Juselius (1990) is considered superior to the Engle-Granger method. This approach provides a

multivariate framework and allows for more than one cointegrating vectors. Johansen and

Juselius (1990) have derived two tests for cointegration, namely, the Trace test and the

Maximum Eigen value test. The computed Trace and Maximum Eigen value test statistics vis--

vis their corresponding critical values are presented in Table 4.3.

Starting with the null hypothesis of no cointegration among the variables, trace statistics

is above the 5 percent critical value. Hence it rejects the null hypothesis of no cointegration, in

13 See Appendix A, table 3, Pg# 25


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favor of general alternatives one cointegrating vector. Turning to the Maximum Eigen value test,

the null hypothesis of no cointegration is rejected at 5 percent level of significance in favor of

specific alternative, that there is one cointegrating vector.

Table 4.314

Cointegration test results

Null Hypothesis Trace 5% Critical Max Eigen 5% Critical

No. of CE(s) Statistics Value Value Statistics Value

None* 80.92335 35.01090 59.10522 24.25202

At most 1 33.32920 18.39771 24.36528 17.14769

At most 2 15.34002 3.841466 16.54022 3.841466


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

graph 4.4. In CUSUM test the results within 2 standard deviations but CUSUM of Squares test

shows fluctuation in 2006, 2009 and outsides the 2 standard deviations. So, we can confirm this

through chow breakpoint test as shown in the table 4.5

Graph 4.1

CUSUM AND CUSUM OF Square test

14 See Appendix A, Table 15, Pg #45


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-40

-30

-20

-10

0

10

20

30

40

25 50 75 100

CUSUM 5% Significance

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

25 50 75 100

CUSUM of Squares 5% Significance

The reason behind this the previous financial crisis in the West is characterized by contracted

liquidity in global credit markets and banking systems. In the staring weeks of this crisis, the

financial market experienced a mild shock as rumors about freezing of foreign currency accounts

and seizure of bank lockers by the government spread over the country. Thus, the ripple effect of


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shocks from the credit squeeze in the West would have an impact on local financial markets

through these banks.

Table 4.415

Chow breakpoint DummyVariable

Prob.F(12,105) 0.0000 Prob 0.5760

F-statistics 85.476 Coefficient 237342.2

Variable Centered VIF

CA 6.406

HC 6.406

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

year 2006 and the prob value that is less than 0.1which means that there is a change in

coefficient before and after 2006 and we can reject our hypothesis.

After inserting effect of as a dummy variable the coefficient shows the positive

relationship with the capital structure and in table 4.5 prob values is insignificant.

In regression this is imp that there is a minimum correlation between the variables. By running

variance inflation factor we find that in our model the problem of multicollinearity does not exist

because, in the table 4.4 centered VIF column all values have less than 10. First we take log of

all variable then run regression results shows following values which can be estimated in terms

of percentage are shown in the table 4.6.

In the table 4.6 shows that there is a significant impact of log of Human Capital and

Capital Employed on log of Value Added. Both Log of Capital employed and Human capital

shows the positive relationship on log of Value added as well as, R value predicts the 78%

variation in the model.

Table 4.516

OLS Test of Log Variables

15 See Appendix A, table # 10, pg # 29

16 See Appendix A, table#13, pg# 44


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Variables Coefficient t-Statistic Prob

C -3.238926 -3.067588 0.0028

LHC 0.880588 9.024963 0.000

LCA 0.346123 3.563585 0.0006

Prob.F 0.000 Adj. R 0.782849Source: Authors Estimation

With the help of descriptive statistics we can easily identify the mean, median, maximum and

minimum of data as shown in the table 4.7.

Table 4.617

Descriptive Statistics

VA HC CA

Mean 9045059 3470038 27565811

Median 4460129 1915446 13317729

Maximum

4961327

7

1927214

6 1.31E+08

Minimum

-

15304975

0.00000

0 -5599919

In the table 4.7 Capital employed has highest mean and median which shows the centre of data

location. As well as, capital employed also have highest maximum value while Value Added

have minimum value so, with the help of this we can easily identify the highest value and the

lowest value in our data respectively for the VA,CA and HC variables.

5. Causality Analysis

The direction of causality between Value Added, capital employed and human Capital is

identified by using Granger causality method. The usual Granger causality leads to spurious

regression results unless the variables in level are cointegrated. Also Granger causality deals with

17 The descriptive statistics of Value added, human capital and capital employed.


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bivariate regression model. On the other hand Toda and Yamamoto (1995) procedure uses a

modified Wald (MWALD) test which can be applied irrespective of order of integration and also

deals with multivariate regression model.

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 reject our hypothesis: VA does not Granger Cause CA because of

the prob value that is 0.0597 and as well as, we accept our hypothesis VA does not Granger

Cause HC because of the prob value that is 0.2290.

Table 5.118

Causality test results

Source: Author Estimation

18 See Appendix A table 17, page No. 51

Dependent

Variables VA CA HC

VA - 3.61535 1.46217

-

(0.0597

) (0.2290)

CA 0.00550 -

0.59748

(0.4411)

(0.9410) -

HC

0.1656

0

0.77557

(0.3803)

-

(0.6848)-


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We reject our hypothesis: CA does not Granger Cause VA because of the prob value that is

0.9410 and we reject our hypothesis CA does not Granger Cause HC because of the prob value

that is 0.4411. We accept our hypothesis: HC does not Granger Cause VA because of the prob

value that is 0.6848 and we also accept our hypothesis HC does not Granger Cause CA because

of the prob value that is 0.3803.it means that there is a unidirectional casual relationship between

VA and CA thus change in VA cause change in CA.

To identify either fixed model is applied on data or random effect model is correct i.e. their

coefficient value is perfect, we run Haussmann test19whose prob value is less than 0.1 means that

fixed effect model is applicable.

Conclusion:

Intellectual capital plays a vital role in value creation process, especially in case of banking

sector where the value added of the corporation, organization and individual is related to

knowledge and Intellectual capital. This study represents 20 commercial banks of Pakistan as it

is included cooperation, insurance companies, and securities firms. It is estimated that there is a

normal, strong, significant and positive correlation between VA and CA but especially with HC.

The time 2006- 2011 seems to be positive for all because all values are constantly increasing.

this shows that both domestic and foreign commercial banks In Pakistan is largely attributed to

HC in the value creation process means that investment in HC yields a higher return than

investment in CA.

Although the data set not very large therefore for further research it is highlighting the fact that

Intellectual capitalist also contribute the corporate sector especially to its value creation.

19 See Appendix A, Table # 19, page # 53


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BIBILIOGRAPHY

[1] Meng-Yuh Cheng, Jer-Yan Lin, Tzy-Yih Hsiao, Thomas W. Lin, (2010),"Invested resource,

competitive intellectual capital, and corporate performance", Journal of Intellectual Capital, Vol.

11 Iss: 4 pp. 433 450

[2] Dimitrios G. Mavridis, Pantelis Kyrmizoglou, (2005),"Intellectual capital performance

drivers in the Greek banking sector", Management Research News, Vol. 28 Iss: 5 pp. 43 62

[3] Dimitrios G. Mavridis, (2004),"The intellectual capital performance of the Japanese banking

sector, Journal of Intellectual Capital, Vol. 5 Iss: 1 pp. 92 - 115

[4] Irene Wei Kiong Ting, Hooi Lean, (2009), The intellectual capital performance of financial

institutions in Malaysia, Journal of Intellectual Capital, Vol. 10 No. 4, 2009 pp. 588-599

[5] N-P. Swartz, S. Firer, (2005),"Board structure and intellectual capital performance in South

Africa", Meditari Accountancy Research, Vol. 13 Iss: 2 pp. 145 - 166


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[6] Stevo Pucar, (2012),"The influence of intellectual capital on export performance", Journal of

Intellectual Capital, Vol. 13 Iss: 2 pp. 248 261

[7] Abdel-Aziz Ahmad Sharabati, Shawqi Naji Jawad, Nick Bontis, (2010),"Intellectual capital

and business performance in the pharmaceutical sector of Jordan", Management Decision, Vol.

48 Iss: 1 pp. 105 131

[8] Wen-Ying Wang, Chingfu Chang, (2005),"Intellectual capital and performance in causal

models: Evidence from the information technology industry in Taiwan", Journal of Intellectual

Capital, Vol. 6 Iss: 2 pp. 222 236

[9] Robin Roslender, Robin Fincham, (2004),"Intellectual capital accounting in the UK: A field

study perspective", Accounting, Auditing & Accountability Journal, Vol. 17 Iss: 2 pp. 178 209

[10] George Tovstiga, Ekaterina Tulugurova, (2007),"Intellectual capital practices and

performance in Russian enterprises", Journal of Intellectual Capital, Vol. 8 Iss: 4 pp. 695 707

[11] Abdullah Yalama and Metin Coskun (2007), Intellectual capital performance of quoted

banks on the Istanbul Stock exchange market,

[12] Pek Chen Goh (2005), Intellectual capital performance of commercial banks in Malaysia,

Journal of Intellectual capital, Vol. 6 Iss: 3, pp. 385 396.

[13] Abdifatah Ahmed Haji and Sanni Mubarak (2012), Trends of Intellectual capital

disclosure; evidence from the Nigerian bank sector, Journal of Human resource costing andAccounting, Vol. 16 Iss: 3 pp. 184-209.

[14] G. Barathi Kamath (2007), Intellectual capital performance of the Indian banking sector,

Journal of Intellectual capital, Vol. 8 Iss: 1 pp. 96 - 123

[15] Fethi Calisir, Cigdem Altin Gumussoy, A. Elvan Bayraktaroglu and Ece Deniz (2010),

Intellectual capital in the quoted Turkish ITC sector, Journal of Intellectual Capital, Vol. 11

Iss: 4, 2010 pp. 537-553

[16] Nixon Kamukama, Augustine Ahiauzu and Joseph M.Ntyani (2010), Intellectual capital

and performance: testing interaction effects, Journal of Intellectual Capital Vol. 11 Iss: 4, 2010

pp. 554-574


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[17] Subhash Abhayawansa and James Guthrie (2010), Intellectual capital and the capital

market: a review and synthesis, Journal of Human Resource Costing & Accounting, Vol. 14 Iss:

3, pp. 196-226

[18] Magdi El-Bannany (2012), Global financial crisis and the intellectual capital performance

of UAE banks, Journal of Human Resource Costing & Accounting Vol. 16 Iss: 1, pp. 20-36

[19] Amitava Mondal and Santanu Kumar Ghosh (2012), Intellectual capital and financial

performance of Indian banks,, Journal of Intellectual Capital, Vol. 13 Iss: 4,pp. 515-530

[20) Pirjo Stahle, Sten Stahle and Samuli Aho (2011), Value added intellectual coefficient

(VAIC): a critical analysis, Journal of Intellectual Capital Vol. 12 Iss: 4, 2011 pp. 531-551

[21] Edvinsson, L. and Malone, M. (1997), Intellectual Capital: Realising Your Companys True

Value by Finding its Hidden Brainpower, HarperCollins, New York, NY.

[22]Stewart, T. (1997), Intellectual Capital: The New Wealth of Organizations,

Doubleday/Currency, and New York, NY.

[23] Pulic, A. & Bornemann, M. (2002), The physical and intellectual capital of Austrian

banks, available at:www.vaic-on.net

[24] Kaplan, R.S. and Norton, D.P. (1992), The balanced scorecard: measures that drive

performance, Harvard Business Review, Vol. 70 No. 1, pp. 71-9.

Appendix A

1.OLS Regression Table:

Dependent Variable: VAMethod: Least Squares

Date: 04/27/13 Time: 00:06

Sample: 1 120

Included observations: 120

Variable Coefficient Std. Error t-Statistic Prob.

HC 0.697502 0.277896 2.509936 0.0134

CA 0.308621 0.033075 9.330818 0.0000

C -1882696. 566781.2 -3.321734 0.0012

R-squared 0.881905 Mean dependent var 9045059.

Adjusted R-squared 0.879886 S.D. dependent var 13237932
http://www.vaic-on.net/http://www.vaic-on.net/http://www.vaic-on.net/


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S.E. of regression 4587931. Akaike info criterion 33.54044

Sum squared resid 2.46E+15 Schwarz criterion 33.61013

Log likelihood -2009.426 Hannan-Quinn criter. 33.56874

F-statistic 436.8629 Durbin-Watson stat 1.050209

Prob(F-statistic) 0.000000

2.Auto correlation Table:

3. Removal of Autocorrelation:

Cochrane Orcutt Table

Dependent Variable: ER

Method: Least Squares

Date: 04/27/13 Time: 00:11

Sample (adjusted): 2 120

Included observations: 119 after adjustments


Breusch-Godfrey Serial Correlation LM Test:

F-statistic 34.37743 Prob. F(1,116) 0.0000

Obs*R-squared 27.43292 Prob. Chi-Square(1) 0.0000

Test Equation:

Dependent Variable: RESIDMethod: Least Squares

Date: 04/27/13 Time: 00:07

Sample: 1 120

Included observations: 120Presample missing value lagged residuals set to zero.


HC 0.148600 0.246430 0.603010 0.5477

CA -0.023471 0.029448 -0.797018 0.4271

C 131903.7 500444.9 0.263573 0.7926RESID(-1) 0.483389 0.082444 5.863227 0.0000

R-squared 0.228608 Mean dependent var 5.34E-12

Adjusted R-squared 0.208658 S.D. dependent var 4549214.

S.E. of regression 4046863. Akaike info criterion 33.29755Sum squared resid 1.90E+15 Schwarz criterion 33.39046





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26

ER(-1) 0.472931 0.080907 5.845386 0.0000

R-squared 0.224518 Mean dependent var -26139.45




Log likelihood -1977.814 Hannan-Quinn criter. 33.26686Durbin-Watson stat 1.912260

4. Transpose

Dependent Variable: TVA


Date: 04/27/13 Time: 00:14




HC 40272893 10677183 3.771865 0.0003

CA 7683635. 1271004. 6.045328 0.0000

C -1.36E+14 2.18E+13 -6.229449 0.0000

R-squared 0.836593 Mean dependent var 2.16E+14

Adjusted R-squared 0.833776 S.D. dependent var 4.32E+14S.E. of regression 1.76E+14 Akaike info criterion 68.46838



F-statistic 296.9419 Durbin-Watson stat 1.016406Prob(F-statistic) 0.000000

5. LM Test for 1st Transpose of Data

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.976599 Prob. F(1,115) 0.3251Obs*R-squared 1.002058 Prob. Chi-Square(1) 0.3168

Test Equation:

Dependent Variable: RESID

Method: Least SquaresDate: 04/27/13 Time: 00:46

Sample: 2 120


Presample missing value lagged residuals set to zero.


HC -0.002414 0.319745 -0.007551 0.9940CA -0.000252 0.038062 -0.006620 0.9947


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27

C 16317.38 654003.1 0.024950 0.9801

RESID(-1) 0.091845 0.092939 0.988230 0.3251

R-squared 0.008421 Mean dependent var -5.67E-10

Adjusted R-squared -0.017447 S.D. dependent var 5231887.


Sum squared resid 3.20E+15 Schwarz criterion 33.92219Log likelihood -2008.812 Hannan-Quinn criter. 33.86671F-statistic 0.325533 Durbin-Watson stat 2.015465


6. Multicollinearity test Table:

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

Colline

B Std. Error Beta Toleran

1 (Constant) -1882696.145 566781.151 -3.322 .001

Human Capital(Salaries

Allowance)

.698 .278 .202 2.510 .013 .156

capital employed (NET

PROFIT + EQUITY)

.309 .033 .750 9.331 .000 .156

a. Dependent Variable: VALUE ADDED (REV- EXP)

7. Heteroskedasticity Test:

Heteroskedasticity Test: White


Obs*R-squared 18.06675 Prob. Chi-Square(5) 0.0029

Scaled explained SS 47.78135 Prob. Chi-Square(5) 0.0000

Test Equation:Dependent Variable: RESID^2


Sample: 1 120



C 5.74E+12 7.47E+12 0.768964 0.4435

CA 1041790. 662631.8 1.572200 0.1187CA^2 0.040565 0.016327 2.484500 0.0144

CA*HC -0.768581 0.239186 -3.213325 0.0017

HC -3746368. 4802410. -0.780102 0.4369

HC^2 3.249272 1.057080 3.073818 0.0026

R-squared 0.150556 Mean dependent var 2.05E+13Adjusted R-squared 0.113300 S.D. dependent var 4.86E+13

S.E. of regression 4.58E+13 Akaike info criterion 65.79617



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28




8. Heteroskedasticity Removal:

Heteroskedasticity Test: White


Obs*R-squared 2.248177 Prob. Chi-Square(2) 0.3249Scaled explained SS 6.477796 Prob. Chi-Square(2) 0.0392

Test Equation:

Dependent Variable: RESID^2


Date: 04/27/13 Time: 08:17

Sample: 1 120



C 1.898468 0.473383 4.010426 0.0001

(HC/AX)^2 0.240953 0.167455 1.438912 0.1528(CA/AX)^2 -2.11E-07 5.63E-07 -0.373866 0.7092

R-squared 0.018735 Mean dependent var 2.036071

Adjusted R-squared 0.001961 S.D. dependent var 5.034066

S.E. of regression 5.029128 Akaike info criterion 6.093052

Sum squared resid 2959.179 Schwarz criterion 6.162740




9. Stability Test:

-40

-30

-20

-10

0

10

20

30

40

25 50 75 100

CUSUM 5% S igni fi cance

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

25 50 75 100

CUSUM of Squares 5% Significance

10. Chow break Point test:


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29

Chow Breakpoint Test: 25 80 92 98

Null Hypothesis: No breaks at specified breakpoints

Varying regressors: All equation variables

Equation Sample: 1 120


Log likelihood ratio 45.39203 Prob. Chi-Square(12) 0.0000Wald Statistic 48.27401 Prob. Chi-Square(12) 0.0000

11. Dummy Variable:

Dependent Variable: VA


Sample: 1 120



C -2022328. 620583.4 -3.258753 0.0015

CA 0.310598 0.033360 9.310623 0.0000

HC 0.684419 0.279689 2.447076 0.0159DUM 237342.2 423204.0 0.560822 0.5760








At Level Graph (Trend)

-20,000,000

-10,000,000

0

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

25 50 75 100

VA

-20,000,000

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

25 50 75 100

CA


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30

At 1st Difference graph (trend)

-50,000,000

-40,000,000

-30,000,000

-20,000,000

-10,000,000

0

10,000,000

20,000,000

30,000,000

25 50 75 100

D(VA)

-150,000,000

-125,000,000

-100,000,000

-75,000,000

-50,000,000

-25,000,000

0

25,000,000

50,000,000

25 50 75 100

D(CA)

0

4,000,000

8,000,000

12,000,000

16,000,000

20,000,000

25 50 75 100

HC


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-20,000,000

-15,000,000

-10,000,000

-5,000,000

0

5,000,000

25 50 75 100

D(HC)

For log level graph (trend)

9

10

11

12

13

14

15

16

17

18

25 50 75 100

LOG(VA)

12

13

14

15

16

17

18

19

25 50 75 100

LOG(CA)

10

11

12

13

14

15

16

17

25 50 75 100

LOG(HC)


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For Log 1st Difference graph (trend):

-6

-4

-2

0

2

4

6

25 50 75 100

D(LOG(VA))

-4

-3

-2

-1

0

1

2

3

4

5

25 50 75 100

D(LOG(CA))

-5

-4

-3

-2

-1

0

1

2

3

25 50 75 100

D(LOG(HC))

12. Unit root Test:

At level with constant and with constant and trend

Null Hypothesis: VA has a unit root

Exogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.930746 0.0025

Test critical values: 1% level -3.486064

5% level -2.88586310% level -2.579818

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

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(VA)


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Date: 04/27/13 Time: 01:26




VA(-1) -0.234471 0.059650 -3.930746 0.0001

C 2069987. 957584.7 2.161675 0.0327


Adjusted R-squared 0.109103 S.D. dependent var 9115497.S.E. of regression 8603875. Akaike info criterion 34.78999





Null Hypothesis: VA has a unit rootExogenous: Constant, Linear Trend

Lag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*



5% level -3.448021

10% level -3.149135



Dependent Variable: D(VA)





VA(-1) -0.246711 0.061019 -4.043198 0.0001

C 3533705. 1801194. 1.961868 0.0522

@TREND(1) -22538.04 23487.14 -0.959591 0.3393




Sum squared resid 8.59E+15 Schwarz criterion 34.86895Log likelihood -2067.534 Hannan-Quinn criter. 34.82734




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Null Hypothesis: CA has a unit root

Exogenous: Constant


t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.076321 0.0015Test critical values: 1% level -3.486064

5% level -2.885863

10% level -2.579818


Augmented Dickey-Fuller Test EquationDependent Variable: D(CA)


Date: 04/27/13 Time: 01:27




CA(-1) -0.249701 0.061256 -4.076321 0.0001

C 6834368. 2599667. 2.628940 0.0097



S.E. of regression 21480315 Akaike info criterion 36.61984Sum squared resid 5.40E+16 Schwarz criterion 36.66654




Null Hypothesis: CA has a unit rootExogenous: Constant, Linear Trend


t-Statistic Prob.*



5% level -3.448021

10% level -3.149135



Dependent Variable: D(CA)


Date: 04/27/13 Time: 01:27




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CA(-1) -0.251932 0.061631 -4.087747 0.0001

C 8569132. 4436063. 1.931698 0.0558

@TREND(1) -27882.22 57673.25 -0.483451 0.6297



S.E. of regression 21551003 Akaike info criterion 36.63463Sum squared resid 5.39E+16 Schwarz criterion 36.70469

Log likelihood -2176.760 Hannan-Quinn criter. 36.66308F-statistic 8.370645 Durbin-Watson stat 1.990587


Null Hypothesis: HC has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.885863

10% level -2.579818



Dependent Variable: D(HC)Method: Least Squares

Date: 04/27/13 Time: 01:28




HC(-1) -0.248025 0.061231 -4.050641 0.0001

C 846871.6 317043.8 2.671150 0.0086



S.E. of regression 2553201. Akaike info criterion 32.36026Sum squared resid 7.63E+14 Schwarz criterion 32.40697Log likelihood -1923.435 Hannan-Quinn criter. 32.37922



Null Hypothesis: HC has a unit root

Exogenous: Constant, Linear Trend


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t-Statistic Prob.*



5% level -3.44802110% level -3.149135



Dependent Variable: D(HC)





HC(-1) -0.261186 0.062512 -4.178160 0.0001

C 1325781. 559997.0 2.367479 0.0196

@TREND(1) -7215.716 6956.064 -1.037327 0.3017







At 1stDifference with Constant and with Constant and Trend.

Null Hypothesis: D(VA) has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -2.886074

10% level -2.579931


Augmented Dickey-Fuller Test EquationDependent Variable: D(VA,2)


Date: 04/27/13 Time: 01:30




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D(VA(-1)) -1.121591 0.092163 -12.16971 0.0000

C -71499.92 840097.7 -0.085109 0.9323

R-squared 0.560776 Mean dependent var 9582.992Adjusted R-squared 0.556989 S.D. dependent var 13710396



F-statistic 148.1019 Durbin-Watson stat 2.016413Prob(F-statistic) 0.000000

Null Hypothesis: D(VA) has a unit root



t-Statistic Prob.*



5% level -3.448348

10% level -3.149326



Dependent Variable: D(VA,2)


Date: 04/27/13 Time: 01:31Sample (adjusted): 3 120



D(VA(-1)) -1.121734 0.092562 -12.11874 0.0000

C 124148.0 1719662. 0.072193 0.9426@TREND(1) -3234.021 24769.56 -0.130564 0.8963







Null Hypothesis: D(CA) has a unit root

Exogenous: Constant



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t-Statistic Prob.*


Test critical values: 1% level -3.4865515% level -2.886074

10% level -2.579931



Dependent Variable: D(CA,2)


Date: 04/27/13 Time: 01:31




D(CA(-1)) -1.120467 0.092184 -12.15467 0.0000C -115709.1 2106783. -0.054922 0.9563



S.E. of regression 22885194 Akaike info criterion 36.74668





Null Hypothesis: D(CA) has a unit root



t-Statistic Prob.*



5% level -3.448348

10% level -3.149326


Augmented Dickey-Fuller Test EquationDependent Variable: D(CA,2)


Date: 04/27/13 Time: 01:32





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D(CA(-1)) -1.120775 0.092587 -12.10511 0.0000

C 558610.1 4312637. 0.129529 0.8972

@TREND(1) -11146.40 62120.04 -0.179433 0.8579



S.E. of regression 22981263 Akaike info criterion 36.76335Sum squared resid 6.07E+16 Schwarz criterion 36.83379Log likelihood -2166.038 Hannan-Quinn criter. 36.79195



Null Hypothesis: D(HC) has a unit root

Exogenous: ConstantLag Length: 0 (Automatic based on SIC, MAXLAG=1)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -11.67488 0.0000Test critical values: 1% level -3.486551

5% level -2.88607410% level -2.579931



Dependent Variable: D(HC,2)


Date: 04/27/13 Time: 01:33




D(HC(-1)) -1.080406 0.092541 -11.67488 0.0000

C -24913.46 251224.3 -0.099168 0.9212








Null Hypothesis: D(HC) has a unit root



t-Statistic Prob.*


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5% level -3.448348

10% level -3.149326



Dependent Variable: D(HC,2)





D(HC(-1)) -1.080703 0.092947 -11.62715 0.0000

C 52387.84 514249.7 0.101872 0.9190

@TREND(1) -1277.815 7407.478 -0.172503 0.8633







13. Single Log Model:

Dependent Variable: LVA


Sample: 1 120Included observations: 99


HC 1.64E-07 6.65E-08 2.473736 0.0151

CA 1.73E-08 8.07E-09 2.138591 0.0350

C 14.20603 0.149178 95.22870 0.0000


Adjusted R-squared 0.568932 S.D. dependent var 1.592190

S.E. of regression 1.045365 Akaike info criterion 2.956443Sum squared resid 104.9076 Schwarz criterion 3.035083




14. Double Log Variable model:

Dependent Variable: LVA


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Date: 04/27/13 Time: 01:37

Sample: 1 120



LHC 0.880588 0.097572 9.024963 0.0000

LCA 0.346123 0.097128 3.563585 0.0006

C -3.238926 1.055854 -3.067588 0.0028

R-squared 0.787327 Mean dependent var 15.42642Adjusted R-squared 0.782849 S.D. dependent var 1.598962

S.E. of regression 0.745107 Akaike info criterion 2.279557

Sum squared resid 52.74257 Schwarz criterion 2.358689





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15.Cointegration Test:

Date: 05/07/13 Time: 02:05

Sample: 1 120


Series: VA CA HC

Lags interval: 1 to 1

Selected

(0.05 level*)

Number of

Cointegrating

Relations by

Model

Data Trend: None None Linear Linear Quadratic

Test Type No Intercept Intercept Intercept Intercept Intercept

No Trend No Trend No Trend Trend TrendTrace 3 3 3 3 3

Max-Eig 3 3 3 3 3

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

Information

Criteria by

Rank and

Model

Data Trend: None None Linear Linear Quadratic

Rank or No Intercept Intercept Intercept Intercept InterceptNo. of CEs No Trend No Trend No Trend Trend Trend

Log

Likelihood by

Rank (rows)

and Model

(columns)

0 -5973.508 -5973.508 -5973.497 -5973.497 -5973.479

1 -5957.253 -5952.925 -5952.915 -5947.475 -5947.465

2 -5947.493 -5942.992 -5942.986 -5935.860 -5935.854

3 -5943.501 -5936.000 -5936.000 -5928.568 -5928.568

Akaike

Information

Criteria by

Rank (rows)

and Model

(columns)


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43

Date: 04/27/13 Time: 01:38

Sample: 1 120

Lags: 1

Null Hypothesis: Obs F-Statistic Prob.

CA does not Granger Cause VA 119 0.00550 0.9410VA does not Granger Cause CA 3.61535 0.0597

HC does not Granger Cause VA 119 0.16560 0.6848VA does not Granger Cause HC 1.46217 0.2290

HC does not Granger Cause CA 119 0.77557 0.3803

CA does not Granger Cause HC 0.59748 0.4411

18. Panel Regression Model:


Method: Panel Least Squares

Date: 04/27/13 Time: 01:48Sample: 2006 2011

Periods included: 6

Cross-sections included: 20

Total panel (unbalanced) observations: 119


HC 0.700294 0.279359 2.506791 0.0136CA 0.308509 0.033216 9.288024 0.0000

C -1897383. 573436.1 -3.308797 0.0012


Adjusted R-squared 0.879437 S.D. dependent var 13267582S.E. of regression 4606797. Akaike info criterion 33.54885





Random effect model:


Method: Panel EGLS (Two-way random effects)

Date: 05/07/13 Time: 03:31

Sample: 2006 2011Periods included: 6


Total panel (balanced) observations: 120

Swamy and Arora estimator of component variances


C -1258585. 979585.3 -1.284814 0.2014HC 0.787900 0.307149 2.565201 0.0116


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45


C 2696117. 1125132. 2.396268 0.0185

HC 0.058357 0.380019 0.153562 0.8783

CA 0.222973 0.039165 5.693153 0.0000

Effects SpecificationS.D. Rho

Cross-section fixed (dummy variables)Period random 1095659. 0.0900

Idiosyncratic random 3484424. 0.9100

Weighted Statistics



S.E. of regression 3506371. Sum squared resid 1.20E+15



Unweighted Statistics


Sum squared resid 1.35E+15 Durbin-Watson stat 1.715169

Period random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

HC 0.931033 0.787900 -0.008157 NA

CA 0.264678 0.274601 -0.000143 NA

Period random effects test equation:


Method: Panel EGLS (Cross-section random effects)

Date: 05/07/13 Time: 03:32

Sample: 2006 2011

Periods included: 6

Cross-sections included: 20Total panel (balanced) observations: 120

Swamy and Arora estimator of component variances


C -1481737. 617583.0 -2.399252 0.0181

HC 0.931033 0.293570 3.171420 0.0020

CA 0.264678 0.033188 7.975237 0.0000

Effects Specification

S.D. Rho

Cross-section random 2325496. 0.3082

Period fixed (dummy variables)


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46

Idiosyncratic random 3484424. 0.6918

Weighted Statistics



S.E. of regression 3675349. Sum squared resid 1.51E+15F-statistic 60.51133 Durbin-Watson stat 1.299529


Unweighted Statistics


Sum squared resid 2.17E+15 Durbin-Watson stat 0.907509

Cross-section and period random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

HC 0.132065 0.787900 0.078898 0.0196

CA 0.216925 0.274601 0.000301 0.0009

Cross-section and period random effects test equation:

Dependent Variable: VAMethod: Panel Least Squares

Date: 05/07/13 Time: 03:32Sample: 2006 2011

Periods included: 6


Total panel (balanced) observations: 120


C 2607080. 1261248. 2.067063 0.0415

HC 0.132065 0.416220 0.317296 0.7517

CA 0.216925 0.039309 5.518395 0.0000

Effects Specification

Cross-section fixed (dummy variables)Period fixed (dummy variables)



S.E. of regression 3484424. Akaike info criterion 33.16061Sum squared resid 1.13E+15 Schwarz criterion 33.78780





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Appendix B

secondary report

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