secondary report
TRANSCRIPT
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Intellectual Capital performance in the banking sector of Pakistan
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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
Source: Authors Estimation
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
Source: Authors Estimation
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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[17] Subhash Abhayawansa and James Guthrie (2010), Intellectual capital and the capital
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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
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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
Variable Coefficient Std. Error t-Statistic Prob.
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.
Variable Coefficient Std. Error t-Statistic Prob.
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
Log likelihood -1993.853 Hannan-Quinn criter. 33.33528
F-statistic 11.45914 Durbin-Watson stat 1.906952
Prob(F-statistic) 0.000001
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ER(-1) 0.472931 0.080907 5.845386 0.0000
R-squared 0.224518 Mean dependent var -26139.45
Adjusted R-squared 0.224518 S.D. dependent var 4559391.
S.E. of regression 4015065. Akaike info criterion 33.25737
Sum squared resid 1.90E+15 Schwarz criterion 33.28073
Log likelihood -1977.814 Hannan-Quinn criter. 33.26686Durbin-Watson stat 1.912260
4. Transpose
Dependent Variable: TVA
Method: Least Squares
Date: 04/27/13 Time: 00:14
Sample (adjusted): 2 120
Included observations: 119 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
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
Sum squared resid 3.60E+30 Schwarz criterion 68.53845
Log likelihood -4070.869 Hannan-Quinn criter. 68.49683
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
Included observations: 119
Presample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
HC -0.002414 0.319745 -0.007551 0.9940CA -0.000252 0.038062 -0.006620 0.9947
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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.
S.E. of regression 5277329. Akaike info criterion 33.82877
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
Prob(F-statistic) 0.806893
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
F-statistic 4.041096 Prob. F(5,114) 0.0021
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
Method: Least SquaresDate: 05/07/13 Time: 02:32
Sample: 1 120
Included observations: 120
Variable Coefficient Std. Error t-Statistic Prob.
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
Sum squared resid 2.39E+29 Schwarz criterion 65.93555
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Log likelihood -3941.770 Hannan-Quinn criter. 65.85277
F-statistic 4.041096 Durbin-Watson stat 1.533353
Prob(F-statistic) 0.002057
8. Heteroskedasticity Removal:
Heteroskedasticity Test: White
F-statistic 1.116911 Prob. F(2,117) 0.3308
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
Method: Least Squares
Date: 04/27/13 Time: 08:17
Sample: 1 120
Included observations: 120
Variable Coefficient Std. Error t-Statistic Prob.
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
Log likelihood -362.5831 Hannan-Quinn criter. 6.121353
F-statistic 1.116911 Durbin-Watson stat 1.128729
Prob(F-statistic) 0.330753
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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Chow Breakpoint Test: 25 80 92 98
Null Hypothesis: No breaks at specified breakpoints
Varying regressors: All equation variables
Equation Sample: 1 120
F-statistic 4.022834 Prob. F(12,105) 0.0000
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
Method: Least SquaresDate: 04/27/13 Time: 00:56
Sample: 1 120
Included observations: 120
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.882224 Mean dependent var 9045059.
Adjusted R-squared 0.879178 S.D. dependent var 13237932
S.E. of regression 4601430. Akaike info criterion 33.55440
Sum squared resid 2.46E+15 Schwarz criterion 33.64731
Log likelihood -2009.264 Hannan-Quinn criter. 33.59213
F-statistic 289.6404 Durbin-Watson stat 1.051700
Prob(F-statistic) 0.000000
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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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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Method: Least Squares
Date: 04/27/13 Time: 01:26
Sample (adjusted): 2 120
Included observations: 119 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
VA(-1) -0.234471 0.059650 -3.930746 0.0001
C 2069987. 957584.7 2.161675 0.0327
R-squared 0.116653 Mean dependent var -64576.76
Adjusted R-squared 0.109103 S.D. dependent var 9115497.S.E. of regression 8603875. Akaike info criterion 34.78999
Sum squared resid 8.66E+15 Schwarz criterion 34.83670
Log likelihood -2068.004 Hannan-Quinn criter. 34.80895
F-statistic 15.45076 Durbin-Watson stat 2.006152
Prob(F-statistic) 0.000144
Null Hypothesis: VA has a unit rootExogenous: Constant, Linear Trend
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.043198 0.0098
Test critical values: 1% level -4.036983
5% level -3.448021
10% level -3.149135
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(VA)
Method: Least SquaresDate: 04/27/13 Time: 01:26
Sample (adjusted): 2 120
Included observations: 119 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.123610 Mean dependent var -64576.76
Adjusted R-squared 0.108500 S.D. dependent var 9115497.
S.E. of regression 8606788. Akaike info criterion 34.79889
Sum squared resid 8.59E+15 Schwarz criterion 34.86895Log likelihood -2067.534 Hannan-Quinn criter. 34.82734
F-statistic 8.180560 Durbin-Watson stat 1.997471
Prob(F-statistic) 0.000475
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Null Hypothesis: CA has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
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
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(CA)
Method: Least Squares
Date: 04/27/13 Time: 01:27
Sample (adjusted): 2 120
Included observations: 119 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
CA(-1) -0.249701 0.061256 -4.076321 0.0001
C 6834368. 2599667. 2.628940 0.0097
R-squared 0.124359 Mean dependent var -84488.76
Adjusted R-squared 0.116875 S.D. dependent var 22857541
S.E. of regression 21480315 Akaike info criterion 36.61984Sum squared resid 5.40E+16 Schwarz criterion 36.66654
Log likelihood -2176.880 Hannan-Quinn criter. 36.63880
F-statistic 16.61639 Durbin-Watson stat 1.991015
Prob(F-statistic) 0.000084
Null Hypothesis: CA has a unit rootExogenous: Constant, Linear Trend
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.087747 0.0086
Test critical values: 1% level -4.036983
5% level -3.448021
10% level -3.149135
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CA)
Method: Least Squares
Date: 04/27/13 Time: 01:27
Sample (adjusted): 2 120
Included observations: 119 after adjustments
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Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.126120 Mean dependent var -84488.76
Adjusted R-squared 0.111053 S.D. dependent var 22857541
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
Prob(F-statistic) 0.000402
Null Hypothesis: HC has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.050641 0.0017
Test critical values: 1% level -3.486064
5% level -2.885863
10% level -2.579818
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(HC)Method: Least Squares
Date: 04/27/13 Time: 01:28
Sample (adjusted): 2 120
Included observations: 119 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
HC(-1) -0.248025 0.061231 -4.050641 0.0001
C 846871.6 317043.8 2.671150 0.0086
R-squared 0.122989 Mean dependent var -19403.35
Adjusted R-squared 0.115493 S.D. dependent var 2714779.
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
F-statistic 16.40769 Durbin-Watson stat 1.922557
Prob(F-statistic) 0.000092
Null Hypothesis: HC has a unit root
Exogenous: Constant, Linear Trend
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Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.178160 0.0065
Test critical values: 1% level -4.036983
5% level -3.44802110% level -3.149135
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(HC)
Method: Least SquaresDate: 04/27/13 Time: 01:29
Sample (adjusted): 2 120
Included observations: 119 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.131050 Mean dependent var -19403.35
Adjusted R-squared 0.116068 S.D. dependent var 2714779.
S.E. of regression 2552372. Akaike info criterion 32.36783
Sum squared resid 7.56E+14 Schwarz criterion 32.43789Log likelihood -1922.886 Hannan-Quinn criter. 32.39628
F-statistic 8.747204 Durbin-Watson stat 1.915372
Prob(F-statistic) 0.000290
At 1stDifference with Constant and with Constant and Trend.
Null Hypothesis: D(VA) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -12.16971 0.0000
Test critical values: 1% level -3.486551
5% level -2.886074
10% level -2.579931
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(VA,2)
Method: Least Squares
Date: 04/27/13 Time: 01:30
Sample (adjusted): 3 120
Included observations: 118 after adjustments
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Variable Coefficient Std. Error t-Statistic Prob.
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
S.E. of regression 9125510. Akaike info criterion 34.90785
Sum squared resid 9.66E+15 Schwarz criterion 34.95481Log likelihood -2057.563 Hannan-Quinn criter. 34.92692
F-statistic 148.1019 Durbin-Watson stat 2.016413Prob(F-statistic) 0.000000
Null Hypothesis: D(VA) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -12.11874 0.0000
Test critical values: 1% level -4.037668
5% level -3.448348
10% level -3.149326
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(VA,2)
Method: Least Squares
Date: 04/27/13 Time: 01:31Sample (adjusted): 3 120
Included observations: 118 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.560841 Mean dependent var 9582.992
Adjusted R-squared 0.553203 S.D. dependent var 13710396
S.E. of regression 9164421. Akaike info criterion 34.92465
Sum squared resid 9.66E+15 Schwarz criterion 34.99509Log likelihood -2057.554 Hannan-Quinn criter. 34.95325
F-statistic 73.43200 Durbin-Watson stat 2.016447
Prob(F-statistic) 0.000000
Null Hypothesis: D(CA) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
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t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -12.15467 0.0000
Test critical values: 1% level -3.4865515% level -2.886074
10% level -2.579931
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(CA,2)
Method: Least Squares
Date: 04/27/13 Time: 01:31
Sample (adjusted): 3 120
Included observations: 118 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(CA(-1)) -1.120467 0.092184 -12.15467 0.0000C -115709.1 2106783. -0.054922 0.9563
R-squared 0.560166 Mean dependent var 21588.80
Adjusted R-squared 0.556375 S.D. dependent var 34359465
S.E. of regression 22885194 Akaike info criterion 36.74668
Sum squared resid 6.08E+16 Schwarz criterion 36.79364
Log likelihood -2166.054 Hannan-Quinn criter. 36.76575
F-statistic 147.7360 Durbin-Watson stat 2.001663
Prob(F-statistic) 0.000000
Null Hypothesis: D(CA) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -12.10511 0.0000
Test critical values: 1% level -4.037668
5% level -3.448348
10% level -3.149326
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(CA,2)
Method: Least Squares
Date: 04/27/13 Time: 01:32
Sample (adjusted): 3 120
Included observations: 118 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
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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
R-squared 0.560289 Mean dependent var 21588.80
Adjusted R-squared 0.552642 S.D. dependent var 34359465
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
F-statistic 73.26782 Durbin-Watson stat 2.001614
Prob(F-statistic) 0.000000
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
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(HC,2)
Method: Least Squares
Date: 04/27/13 Time: 01:33
Sample (adjusted): 3 120
Included observations: 118 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(HC(-1)) -1.080406 0.092541 -11.67488 0.0000
C -24913.46 251224.3 -0.099168 0.9212
R-squared 0.540235 Mean dependent var -1267.305
Adjusted R-squared 0.536271 S.D. dependent var 4007344.
S.E. of regression 2728906. Akaike info criterion 32.49350
Sum squared resid 8.64E+14 Schwarz criterion 32.54046
Log likelihood -1915.117 Hannan-Quinn criter. 32.51257
F-statistic 136.3028 Durbin-Watson stat 2.011347
Prob(F-statistic) 0.000000
Null Hypothesis: D(HC) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic based on SIC, MAXLAG=1)
t-Statistic Prob.*
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Augmented Dickey-Fuller test statistic -11.62715 0.0000
Test critical values: 1% level -4.037668
5% level -3.448348
10% level -3.149326
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(HC,2)
Method: Least SquaresDate: 04/27/13 Time: 01:33
Sample (adjusted): 3 120
Included observations: 118 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.540354 Mean dependent var -1267.305
Adjusted R-squared 0.532360 S.D. dependent var 4007344.
S.E. of regression 2740391. Akaike info criterion 32.51019
Sum squared resid 8.64E+14 Schwarz criterion 32.58064Log likelihood -1915.101 Hannan-Quinn criter. 32.53880
F-statistic 67.59623 Durbin-Watson stat 2.011315
Prob(F-statistic) 0.000000
13. Single Log Model:
Dependent Variable: LVA
Method: Least SquaresDate: 04/27/13 Time: 01:36
Sample: 1 120Included observations: 99
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.577729 Mean dependent var 15.41969
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
Log likelihood -143.3439 Hannan-Quinn criter. 2.988261
F-statistic 65.67119 Durbin-Watson stat 0.696122
Prob(F-statistic) 0.000000
14. Double Log Variable model:
Dependent Variable: LVA
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Method: Least Squares
Date: 04/27/13 Time: 01:37
Sample: 1 120
Included observations: 98
Variable Coefficient Std. Error t-Statistic Prob.
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
Log likelihood -108.6983 Hannan-Quinn criter. 2.311564
F-statistic 175.8472 Durbin-Watson stat 0.947652
Prob(F-statistic) 0.000000
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15.Cointegration Test:
Date: 05/07/13 Time: 02:05
Sample: 1 120
Included observations: 118
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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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:
Dependent Variable: VA
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
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.881480 Mean dependent var 9121071.
Adjusted R-squared 0.879437 S.D. dependent var 13267582S.E. of regression 4606797. Akaike info criterion 33.54885
Sum squared resid 2.46E+15 Schwarz criterion 33.61891
Log likelihood -1993.157 Hannan-Quinn criter. 33.57730
F-statistic 431.3700 Durbin-Watson stat 0.975828
Prob(F-statistic) 0.000000
Random effect model:
Dependent Variable: VA
Method: Panel EGLS (Two-way random effects)
Date: 05/07/13 Time: 03:31
Sample: 2006 2011Periods included: 6
Cross-sections included: 20
Total panel (balanced) observations: 120
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1258585. 979585.3 -1.284814 0.2014HC 0.787900 0.307149 2.565201 0.0116
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Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.941423 Mean dependent var 9045060.
Adjusted R-squared 0.928871 S.D. dependent var 13147256
S.E. of regression 3506371. Sum squared resid 1.20E+15
F-statistic 75.00100 Durbin-Watson stat 1.733415
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.935082 Mean dependent var 9045060.
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:
Dependent Variable: VA
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
Variable Coefficient Std. Error t-Statistic Prob.
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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Idiosyncratic random 3484424. 0.6918
Weighted Statistics
R-squared 0.790881 Mean dependent var 9045060.
Adjusted R-squared 0.777811 S.D. dependent var 7797169.
S.E. of regression 3675349. Sum squared resid 1.51E+15F-statistic 60.51133 Durbin-Watson stat 1.299529
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.896113 Mean dependent var 9045060.
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
Cross-sections included: 20
Total panel (balanced) observations: 120
Variable Coefficient Std. Error t-Statistic Prob.
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)
R-squared 0.945855 Mean dependent var 9045060.
Adjusted R-squared 0.930718 S.D. dependent var 13237931
S.E. of regression 3484424. Akaike info criterion 33.16061Sum squared resid 1.13E+15 Schwarz criterion 33.78780
Log likelihood -1962.637 Hannan-Quinn criter. 33.41531
F-statistic 62.48508 Durbin-Watson stat 1.740986
Prob(F-statistic) 0.000000
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Appendix B