**FIRM SIZE AND CAPITAL STRENGTH AS DETERMINANTS
OF FIRM PERFORMANCE: SECTOR WISE ANALYSIS OF
KARACHI STOCK EXCHANGE
Zaheer Abbas1, Syed M. Amir Shah2 and Professor Amanullah Khan3
Faculty of Management Sciences International Islamic University, Islamabad1
[email protected]
Allama Iqbal Open University, Islamabad2
Faculty of Management Sciences, International Islamic University, Islamabad3
[email protected]
Abstract
This paper investigates the relationship of firm performance with its size and capital
strength. Unbalanced panel data of twenty different sectors has been analyzed and
contribution of various variables towards firm performance has been estimated using
fixed effects model. The analysis of firms listed on Karachi Stock Exchange (KSE)
ranges over a period of four years starting from year 2003 to year 2006. This study
contributes in existing literature as it results in producing a model, which determines
the drivers of firm performance. Various literatures have used various determinants of
performance but main focus of researchers is on firm size and capital strength. To
avoid wrong attribution of performance with only size and capital strength, dividend
as a measure of financial constraints, net profit margin as a measure of cost control
ability and ratio of financial charges to sales as a measure of financial burden have
been used as explanatory variables. Empirical analysis of twenty industries proves
that firm size and capital strength significantly affect the firm performance, thus,
supports the theories of firm size based on economies of scale that hold that higher
size leads to market power and firms enjoy the economic rents.
Keywords: KSE, Capital Strength, unbalanced panel data
2
1.
Introduction
This paper investigates the relationship of firm performance with its size and capital
strength. Unbalanced panel data of twenty different sectors has been analyzed and
contribution of various variables towards firm performance has been estimated using
fixed effects model. The analysis of firms listed on Karachi Stock Exchange (KSE)
ranges over a period of four years starting from year 2003 to year 2006. Does a
connection exist between the size of a firm, capital strength and firm performance?
According to firm size theories, based on economies of scale, the answer is yes.
However, other most recent theories of firm make different predictions__ including a
prediction that performance has no relationship with firm size or capital strength.
Whether large size firms enjoy the market power and economic rents or not in
Karachi Stock Exchange market and what inter industry differences in firm
performance exist are the questions, which have been empirically addressed in this
study.
This study contributes in existing literature as it results in producing a model, which
determines the drivers of firm performance. Various literatures have used various
determinants of performance but main focus of researchers is on firm size and capital
strength. To avoid wrong attribution of performance with only size and capital
strength, dividend as a measure of financial constraints, net profit margin as a
measure of cost control ability and ratio of financial charges to sales as a measure of
financial burden have been used as explanatory variables in addition to firm size and
capital strength. Firm performance has been measured by return on assets, size has
been measured by natural log of sales, capital strength has been measured by ratio of
equity to total assets, financial constraints have been measured by summation of cash
and stock dividend percentage paid during the year. Firstly, Ordinary Least Square
model has been used. Then same data has been analyzed using fixed effects model
with the assumption that intercept varies among cross sectional units but is time
invariant. Thirdly, fixed effects model has been applied with the assumption that
intercept varies among cross sectional units as well as over time. Comparing the
results of three models, it has been proved that fixed effects model with the
assumption the intercept varies among cross sectional units but is time invariant best
explains the relationship between regressor and regressand in our model.
Empirical analysis of twenty industries proves that firm size and capital strength
significantly affect the firm performance. Secondly, significant inter industry
differences of intercept have been found and captured using industry dummy
variables. Thus this study supports the theories of firm size based on economies of
scale that hold that higher size leads to market power and firms enjoy the economic
rents.
2.
Literature Review
In the recent history, there has been growing interest in determining the relationship
between performance and firm size and performance and capital strength. Different
firm theories often contain implicit assumption about the relationship between size
and performance. These theories may be classified as technological, organizational
and institutional depending on whether they emphasize the production technology
used by the firm, the firms’ organizational architecture and relations among
3
stakeholders or the legal and political environment where the firm operates. These
theories are briefly reviewed as under:2.1 Technological Theories
Technological theories emphasize physical capital and economies of scale and scope
as factors that determine optimal firm size and, by implication, performance. These
theories focus on the production process and the investment in physical capital
necessary to produce output. Increasing economies of scale that permits lumpy fixed
costs to be spread over large output volumes, thereby decreasing the average cost of
production and increasing the return on capital invested, are associated with increases
in firm size. If no limit exists to economies of scale, the unregulated outcome would
be one firm and a natural monopoly. However, if economies of scale cease to exist, at
that point bigger is no longer better, at least in terms of lowering production costs and
improving efficiency. The relationship between firm size and performance due to
economies of scale is depicted in Figure 1.
Whether efficiency and performance eventually fall (average costs increase) as firms
expand under a pure technological story is unclear. One can assert that they do due to
diseconomies of scale; but, the question then arises as to what causes these
diseconomies. Organizational theories enter the picture here.
2.2 Organizational Theories
Organizational theories tie performance and size together with organizational
transaction costs (Williamson, 1985) agency costs (Jensen and Meckling, 1976) and
span of control costs. Transaction costs are the costs of planning, adapting and
monitoring task completion and performance in an organization. These costs include
drafting and negotiating agreements as well as the costs of dealing with disputes and
handling unintended outcomes.
Agency costs arise out of conflicts of interest among the stakeholders of the firm due
to information asymmetries and self-seeking behavior. The underlying assumption
for publicly owned firms is that managers and employees will seek to grow the firm
even if it means making investments that do not cover their cost of capital because
managerial and employee salaries, employment opportunities, perks and employment
security are related to firm size. Growing the firm is also equated with increasing
layers of management and administrative staff which reduces the ability of the
company to quickly respond to changing competitive conditions and to “log-rolling”
within the firm’s bureaucracy with rewards more a function of politicking than of
performance.
Other things being equal, the greater the span of control (number of administrative
layers) in an organization, the greater will be the transaction and agency costs. A
common proxy for the number of administrative layers is the number of employees.
So, organizational theories of the firm grounded in transactions and agency costs and
span of control costs predict that at some point average per unit transaction and
agency costs would increase and offset economies of scale and scope thus establishing
an optimal size for the firm in terms of performance.
4
Critical resource theories of the firm emphasize the control that an entrepreneur or
owner has over those resources – assets, technology, intellectual property – as
determinants of firm size. Kumar, Rajan and Zingales find that as legal institutions
and laws improve the protection afforded the owner of the company over these critical
resources, the size of the firm increases. Rajan and Zingales go on to construct a
model that ties firm size to the ability of the entrepreneur to maintain control over the
intangible factors that make the firm profitable. The greater the importance of these
intangible factors (relative to, say, fixed assets such as machinery) the less likely the
firm is to grow (become larger). So, critical resource theories also tie firm size and
performance together in such a way that at some point, increased size leads to lower
profits. However, under a critical resource theory of the firm “small” firms need not
necessarily be less profitable than “large” firms within a given institutional
environment.
Competency theories of the firm posit that the firm is a collection of competencies
that allow it to earn more than its opportunity cost of capital (surplus, economic rents,
positive net present value projects). These competencies can include superior
production technologies, superior marketing skills, superior research and development
skills and so on. The important point is that one or more of these competencies
allows the firm to remain competitive and earn more than an adequate return. But, in
order for the firm to protect its position, it must make sure other companies do not
acquire its superior competencies – also called secrets.
At this point, competency theories join critical resource theories. Think of
competencies as the critical resources. One way to control the dissemination of
secrets is to share them with as few people as possible and this implies restricting the
size of the firm where size is defined in terms of employees. So, this need to protect
the secrets of the firm places a limit on its size.
Competency theories, however, do not assume that small firms are more or less
profitable than large firms (at or less than the size where secrets are disclosed). One
of the appealing attributes of competency theory is that a “small” firm can be just as
profitable as a “large” firm in a given industry because the firms have different
competencies that let them both earn surplus returns. As described by Niman,
“Survival depends not on being better, but rather on being sufficiently different [due
to different competencies] so that the advantages of others do not prove fatal.” In
fact, a “small” firm may be more profitable than a “large” firm within its product
niche due to its unique competencies. The reason the “small” firm does not grow is
attributed to a “small” market for its product or services and/or to the loss of its
secrets.
2.3 Institutional Theories
Institutional theories tie firm size to such factors as legal systems, anti-trust
regulation, patent protection, market size and the development of financial markets.
Kumar, Rajan and Zingales report, for example, that capital-intensive firms are larger
in countries with efficient judicial systems and that R&D intensive industries have
larger firms in countries with stronger patent protection.
We have restricted our investigation to firms listed at Karachi Stock Exchange and as
different industries are facing different institutional factors such as regulations etc
therefore, sector specific characteristics of performance have been captured in this
5
study. So to some extent we have controlled these factors. Fred R. Kaen (2003) writes
that the basic implication of combining the technological and organizational theories
emphasizing transaction and agency costs of firm size is that within a specific
industry (common production technology) and within a common institutional
environment, firm size and performance may be linked through a trade-off of
economies of scale, transactions costs and agency costs. Essentially, the story is the
following:
Through some initial range, economies of scale lead to lower average unit costs. The
benefits from these lower costs can be distributed among all the stakeholders of the
firm or, through competitive pressures, lead to lower product prices. Let’s take the
case of competitive markets where the cost savings are passed on to the company’s
customers in the form of lower prices. The firm with the lowest unit production costs
can charge the lowest prices. If unit costs are a decreasing function of size and the
product of small firms is identical to that of large firms, small firms will have to
charge the same (or lower) price than large firms resulting in lower per unit profits
and a lower return on investment. Alternatively, at that point where economies of
scale no longer exist, average unit costs would be unrelated to firm size. Then, one
might observe, for example, medium and large firms being equally profitable, as is
depicted in Figure 1 where the graph for the line labeled economies of scales becomes
level.
Now, let’s introduce transaction, agency and span of control costs, which we call
organizational costs. As a firm grows, these costs increase and offset any economies
of scale. In Figure 1, we have labeled the graph that depicts these trade offs the
“combined economies of scale and organizational costs profit function.” The
difference between this line and the economies of scale line is labeled organizational
costs. At that size where economies of scale cease, continued growth results in ever
higher organizational costs and higher unit costs. So, overall performance falls. In
essence, these organization costs place limits on how large a firm can grow in a
competitive market where the governance of the firm is organized around the
objective of owner wealth maximization.
Introducing critical resource and competency theories does, however, complicate this
story. Both critical resource and competency theories imply a limit on firm size either
directly so as to maintain secrets or indirectly through the size of the market for the
firm’s goods and services. Competency theories, in particular, pose a problem because
the competencies firms possess may be different means of production – the
production functions of the technological theories. Therefore, overlaying a
competency theory on a technological theory may or may not result in a prediction
that size and performance are negatively correlated. No relation may exist. Whether
firms with “secret” competencies incur transaction or agency costs, as they become
larger is another matter. If they do, size and performance are negatively correlated; if
not, again, no relation exists.
In Pakistan, no such study has been undertaken, which fully address the relationship
of performance with firm size and capital strength in the light of different firm
theories. This question has been fully addressed and furthermore, sector wise
variations have been captured in this study.
6
3.
Data and Descriptive Statistics
Data used in this study has been collected from various sources but major chunk has
been obtained from analysis reports published by Karachi Stock exchange. The basis
for selection of industry (as depicted in Table 1) in this analysis is that a particular
industry should have at least five firms listed at KSE during period from 2003 to
2006. Secondly, due to unavailability of data, insurance sector has been excluded
from this study. Thirdly, miscellaneous sector has not been incorporated as we have
assumed that intercept and coefficient do not vary within the industry and due to total
different operational nature of firms, this assumption does not hold true, as a result of
which, miscellaneous sector has been excluded. Following the above criteria, we are
left with following twenty industries.
Industry
Year
20
2003
20
2004
20
2005
20
2006
TOTAL NO OF OBSERVATIONS=1671
Table 1: The Observed Industries
Close end Mutual fund
Leasing
Commercial Banks
Textile Weaving
Synthetic and Rayon
Cement
Engineering
Automobile parts
Technology & Communication
Chemical
No. of Firms
460
409
414
388
Modaraba
Investment Banks
Textile Spinning
Textile Composite
Sugar and Allied Industries
Power generation & distribution
Automobile Assembler
Cable & Electrical
Pharmaceutical
Paper & Board
3.1
Performance
Different accounting ratios are available in literature to assess the performance of the
company. It may be measured by return on equity or return on assets etc. In this
analysis, we have used return on assets as a measure of firm performance
N .I t
---------------------------------------------------------(I)
Performance=
T . At
Where
N.It is net income during year t and
T.At is total assets during year t
This variable has been used as dependent variable in this study
3.2 Firm Size
The size of a firm may be measured in number of ways: assets, sales, number of
employees and value added are commonly used measures. Technological theories of
the firm that focus on economies of scale arising out of capital inputs would argue for
using assets or sales as a measure of size. However, assets or sales are not especially
7
good measures of size for organizational theories of the firm. With these theories, the
primary concern is with how transactions, agency and span of control costs affect
performance – costs that are associated primarily with how the organization is
controlled through a hierarchy rather than with the value and number of physical
assets. So, value added and number of employees is better candidates for measuring
firm size for organizational theories than either assets or sales.
The advantage of value added is that it captures the complexity of an organization.
Typically, complexity is associated with the need for more highly skilled employees
and greater coordination and control costs. The implication is that the span of control,
contracting and monitoring costs are likely to be higher for more complex operations
than less complex operations.
The disadvantage of value added is that it is difficult to measure objectively. No of
employees is also one measure of firm size. In our analysis, we have used sale size as
measure of firm size. For scaling purpose, natural log of total sales during current
period have been used. It has positive expected sign. The higher the firm size, the
higher will be the performance and vice versa. This is because higher firm size leads
to economies of scale, where fixed cost is spread over larger number of output units
and thus increasing the performance. Secondly, according to technological firm
theory, higher size will result into economies of scope in the form of product
diversification, which ultimately helps firms in increasing their performance.
Furthermore, economies of scale might reduce the cost of gathering and processing
information and provide access in the markets that smaller firms cannot enter.
Firm Size= Ln (Total sales in year t) = Natural log of Sales ______________(II)
3.3
Capital Strength
CAPSTR is equity to total assets ratio used to measure the capital strength of a
particular firm, calculated as under
Eqt
------------------------------------------------(III)
Capital Strength=
T . At
Where
Eqt is firm equity during year t and
T.At is value of total assets in year t
Higher capital strength ratios are assumed to be indicators of low leverage and
therefore lower risk. Higher ratio means higher portions of assets have been financed
by equity and firm has access to comparatively less expensive source of finances.
Thus it has positive expected sign.
3.4 Net Profit Margin
A particular firm may have higher asset turnover but due to mismanagement or
inefficiency in cost control has low net profit margin. Net profit margin is calculated
as ratio of net income to sale in a particular year t.
N.I t
Net Profit Margin=
-------------------------------------------(IV)
Salest
3.5 Financial Burdening and Financial Constraints
8
In addition to firm size, capital strength and net profit margin, financial constraints
and financial burdening, faced by a firm in particular year t, may also affect the
performance of the firm. Therefore, impact of these has also been incorporated in
estimating the determinants of performance. Financial constraints have been estimated
by summation of cash and stock dividend percentage paid by company during a
particular year. The higher the ratio of dividend, the lesser will be the financial
constraints and higher performance. Financial burdening has been estimated by ratio
of financial charges paid during a given year to total sales. The higher this ratio, the
higher will be financial burdening and vice versa.
Table 1: Descriptive Statistics of Variables
ROA
FSIZE
CAPSTR
FINCHG
DIV
NPMGN
Mean
0.042385
6.284287
0.223891
0.198722
0.179772 -0.472692
Median
0.025165
6.513141
0.267125
0.028905
0.050000 0.042524
1.636039
12.12754
1.147312
108.8824
6.600000 25.43816
-2.164493
Minimu
m
Std. Dev. 0.143409
-3.963316
-13.20548
-0.023682 0.000000 -385.5072
2.032926
0.688031
3.012735
Maximu
m
0.421610 12.93458
Where
ROA is return on assets measured as net income divided by total assets
FSIZE is firm size measured as natural log of total sales
CAPSTR is capital strength measured as ratio of net equity to total assets
FINCHG is a measure of financial burdening calculated as ratio of financial charges
paid by a firm during a particular year to sale
DIV is sum of cash and stock dividend percentage used as a measure of financial
constraints faced by firm
NPMGN is net profit margin used as a measure of cost control efficiency
4.
Methodology and Results
The purpose of this study was to estimate the determinant role of firm size and growth
in performance of firms listed on Karachi Stock Exchange and to develop a model
that takes inter industries differences into account. To meet this objective, four-year
panel data of twenty industries was gathered and empirically analyzed. The data
ranged from year 2003 to 2006. Two models have been applied and results compared.
On the basis of results, Fixed Effects model is considered the best fit. Firstly,
assuming that basic assumptions of classical linear regression model hold true, data
was analyzed using Ordinary Least Square method. The output is shown in Table 2.
Yit = α 1 + ∑ β 1 X it + µ t --------------------------------------------(V)
Where
∑ X it is set of explanatory variables
µ t is error component in year t
9
Equation (V) may be written as
ROAit=∝1+β1.Fsize+β2.Capstrit+β3.Finchgit+β4.Divit+β5.Npmgnit+µit-------------(VI)
Where
ROA is return on assets measured as net income divided by total assets
Fsize is firm size measured as natural log of total sales
Capstr is capital strength measured as ratio of net equity to total assets
Finchg is a measure of financial burdening calculated as ratio of financial charges
paid by a firm during a particular year to sale
Div is sum of cash and stock dividend used as a measure of financial constraints
faced by firm
Npmgn is net profit margin used as a measure of efficiency
Table 2: Output of Ordinary Least Square (OLS) Method
Variable
Coefficient
Std. Error
FSIZE
-0.001770
0.001647
CAPSTR
0.052438
0.004616
FINCHG
0.001092
0.001055
DIV
0.076799
0.007753
NPMGN
0.003080
0.000248
C
0.029203
0.010550
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.210029
0.207657
0.127653
27.13173
1071.610
1.530699
t-Statistic
-1.074586
11.35894
1.034335
9.905287
12.44480
2.768028
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
Prob.
0.2827
0.0000
0.3011
0.0000
0.0000
0.0057
0.042385
0.143409
-1.275415
-1.255950
88.53447
0.000000
ROAit=∝1+β1.Fsize+β2.Capstrit+β3.Finchgit+β4.Divit+β5.Npmgnit+µit
OLS model, here, explains 21% of total variation of performance. Adjusted R2 is not
up to satisfactory level and is 20%. Firm size and ratio of financial charges to sales
have been found insignificantly affecting the firm performance. The Durbin Watson
stat is not close to 0 or 4 showing that our variables are not strongly positive or
negatively correlated. However OLS, here, is subject to very much complications. It
has been applied on the basis of many assumptions, which may not hold true. For
example, the intercept may vary over individual and slope coefficient may be
constant. Secondly, intercept may vary over individuals as well over time or may be
constant among individuals and vary across time. To test and incorporate all these
possibilities, Fixed Effects Model has been applied assuming:1. Slope coefficients are constants but intercept varies across individuals or cross
sectional units
2. Slope coefficients are constant but intercept varies over individuals as well as over
time.
The findings and comments of Judge et al have guided us in the selection of fixed
effects model and not the random effects model. In our analysis, it will be very weak
assumption if we say that error term and explanatory variables are not correlated. In
case error term and explanatory variables are not correlated, Error Component or
Random Effect Model may be appropriate. But if these are correlated then Fixed
10
Effects Model is comparatively better choice. Secondly, when N is large and T is
small, the estimates obtained by FEM and REM can differ significantly. In ECM, we
treat error term as cross sectional random component whereas in FEM, we treat error
component as fixed and not random. We believe that cross sectional units are not
random drawings from large sample; therefore, following the Judge et al suggestions,
Fixed Effects Model is appropriate. If error component and one or more regressors are
correlated, then results obtained from ECM are biased, whereas those obtained from
FEM are unbiased again justifying the use of Fixed effects model in our case. Results
of Fixed Effects Model assuming that intercept varies across individuals and constant
across time i.e. time invariant intercept are shown as Table 3.
Yit = α 1i + ∑ β1 X it + µ it ------------------------------------(VII)
The subscript i on the intercept means that intercept of twenty industries may be
different may be different. This difference may be due to special features of industries
like managerial philosophy or style.
The Equation (VIII) may be written as
ROAit=∝1+α2.D2i+α3.D3i+--------α20.D20i+β1.Fsize + β2.Capstrit + β3.Finchgit + β4.Divit +
β5.Npmgnit+µit------------------------------------(VIII)
Where
D2i---D20i are dummy variables used to capture inter industry intercept differences
while all other variables are same as in equation (VI)
Table 3: Output of Fixed Effects Model, assuming intercept varies
individuals but constant over time
Variable
Coefficient
Std. Error
t-Statistic
FSIZE
0.006317
0.001939
3.257446
CAPSTR
0.029196
0.004767
6.125235
FINCHG
0.001807
0.000981
1.842549
DIV
0.066665
0.007885
8.455077
NPMGN
0.002884
0.000230
12.56187
D1
-0.194201
0.017055
-11.38682
D2
-0.225441
0.018896
-11.93079
D3
-0.241331
0.019272
-12.52216
D4
-0.267837
0.020905
-12.81202
D5
-0.236619
0.015715
-15.05719
D6
-0.264247
0.025112
-10.52259
D7
-0.245524
0.017078
-14.37654
D8
-0.215100
0.021845
-9.846757
D9
-0.243784
0.017965
-13.56972
D10
-0.215976
0.019962
-10.81935
D11
-0.258682
0.022626
-11.43308
D12
-0.191957
0.023726
-8.090685
D13
-0.206373
0.023126
-8.923820
D14
-0.207204
0.023074
-8.979876
D15
-0.284171
0.027406
-10.36886
D16
-0.192041
0.024812
-7.739739
D17
-0.169698
0.024905
-6.813858
across
Prob.
0.0011
0.0000
0.0656
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
11
D18
D19
C
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
-0.242295
-0.181495
0.204577
0.332195
0.322458
0.118044
22.93590
1211.974
1.757448
0.019211
0.023581
0.015932
-12.61262
-7.696657
12.84027
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.0000
0.0000
0.0000
0.042385
0.143409
-1.420675
-1.339568
34.11636
0.000000
ROAit=∝1+α2.D2i+α3.D3i+---α20.D20i+β1.Fsize+β2.Capstrit+β3.Finchgit+β4.Divit+β5.Npmgnit+µit
As we are analyzing twenty industries, therefore, have captured the inter industry
difference in intercept using dummy variables. To avoid the dummy variable trap,
twenty-nine dummy variables from D1 to D19 have been used as explanatory
variable. Above table clearly shows that in comparison with OLS Results, value of R2
has significantly increased when we captured inter industry intercept differences.
Firm size, capital strength, Dividend and Net profit margin have positive coefficient
sign and their respective P values show that their impact on performance is
significant. While financial charges ratio to sales, which has been used as a measure
of financial burdening, has insignificant impact on firm performance. Value of Durbin
Watson stat of 1.75 negates the probability of contamination of results due to positive
or negative correlation. Coefficients of all dummies have been found significant with
very low p values. This means that no two industries in our analysis have similar
coefficients. The constant value of 12.84 is intercept value of close-ended mutual
funds, which we have used as base in our analysis. Summing or subtracting the
individual dummy coefficient from this base coefficient can calculate the intercept
values of other industries.
In the second case of fixed effects model wherein, we assume that intercept varies
across individuals as well as over time. Time is important factor and due to different
kinds of economic policies and due to different status of economic factors, the
relationship of performance with size may be changed.
The Output of said regression is shown table 4.
ROAit=∝1+α2.D2i+α3.D3i+--------α20.D20i+ϒ0+ϒ1.D04+ϒ2.D05+ϒ3.D06+β1.Fsize+
β2.Capstrit + β3.Finchgit + β4.Divit + β5.Npmgnit+µit----------------------------------(IX)
Where
+ϒ0+ϒ1.D04+ϒ2.D05+ϒ3.D06 are time dummies used to capture the difference of
intercept due to time and all other variables are same as in equation (VIII)
Table 4: Output of Fixed Effects Model,
individuals as well as over time
Variable
Coefficient
FSIZE
0.006619
CAPSTR
0.029206
FINCHG
0.001788
DIV
0.066561
NPMGN
0.002879
assuming intercept varies across
Std. Error
0.001961
0.004767
0.000982
0.007890
0.000230
t-Statistic
3.374917
6.127080
1.820736
8.436609
12.52407
Prob.
0.0008
0.0000
0.0688
0.0000
0.0000
12
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D12
D13
D14
D15
D16
D17
D18
D19
D04
D05
D06
C
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
-0.193531
-0.224871
-0.240708
-0.267507
-0.236296
-0.263900
-0.245280
-0.215400
-0.243728
-0.215799
-0.258700
-0.191749
-0.206427
-0.206628
-0.284311
-0.191958
-0.170089
-0.242078
-0.181738
-0.004079
-0.000944
-0.013015
0.206709
0.333424
0.322470
0.118043
22.89371
1213.512
1.759924
0.017090
0.018909
0.019294
0.020917
0.015731
0.025121
0.017096
0.021857
0.017980
0.019977
0.022636
0.023740
0.023134
0.023095
0.027416
0.024860
0.024916
0.019229
0.023598
0.008078
0.008095
0.008237
0.016171
-11.32440
-11.89233
-12.47560
-12.78874
-15.02139
-10.50518
-14.34716
-9.855075
-13.55519
-10.80260
-11.42867
-8.076912
-8.923194
-8.946916
-10.37022
-7.721700
-6.826415
-12.58937
-7.701541
-0.504961
-0.116565
-1.579992
12.78272
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.6137
0.9072
0.1143
0.0000
0.042385
0.143409
-1.418926
-1.328086
30.43831
0.000000
ROAit=∝1+α2.D2i+α3.D3i+---α20.D20i + ϒ0 + ϒ1.D04 + ϒ2.D05 + ϒ3.D06 + β1.Fsize +
β2.Capstrit + β3.Finchgit + β4.Divi t+ β5.Npmgni t+ µit
In table 4, D04, D05, D06 have been used as year dummies to capture the effect of
time over the relationship of firm performance with firm size and capital strength,
while year 03 has been used as base year. Important to note in above table is that none
of the year dummies is constant as probability values of their coefficients are high,
thus, we are back to equation (VIII) where we assumed that intercept varies across
individuals but is time invariant.
5.
Conclusion
The objective of this study was to design an empirical model for performance of firms
listed on Karachi Stock Exchange. For this purpose, twenty industries were selected
and empirically analyzed using fixed effect model of panel data. The period of
analysis ranges over four years from year 2003 to year 2006. A significant
relationship of firm performance with firm size, capital strength and net profit margin
has been empirically determined. But time variable has been found insignificant
meaning that nature of relationship of dependent and explanatory variables does not
13
change over time i.e time invariant intercept. The magnitude and direction of
relationship of firm size with performance is subject to different theories of firm.
According to technological theories of firm, using economies of scale and scope, large
size firms enjoy higher performance. However, according to organizational theories of
firm, other things being equal, the greater the span of control, the greater will be the
agency and transaction cost and the average cost per unit would offset the effects of
economies of scale and scope, thus establishing an optimal size for the firms in terms
of performance. We have empirically found that technological theory of firms hold
true in Pakistan and higher span of control cost, agency cost or transaction costs do
not significantly offset the effects of economies of scale and economies of scope or
we can say that in Pakistan firm size has not reached that level where organizations
costs offset the effects of economies of scale and economies of scope. Furthermore, it
has been proved that those firms, which have higher capital strength i.e. having
financed major part of their assets by equity instead of debt have positive relationship
with performance. This proves that those firms, which have higher capital strength,
have higher capacity to borrow and show higher performance.
References
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14
Schmalensee, R. 1989. Intra-Industry profitability differences in US manufacturing:
1953-1983, Journal of Industrial Economics, 37, 337-357.
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York).
Figure 1
PREDICTED RELATION BETWEEN PROFITABILITY AND SIZE
Trade-off Between Economies of Scale and Organizational Costs
Economies of scale
Organizational costs
Profitability Measure
Optimal firm size
Combined economies of
scale and organizational
costs profit function
-
0
Size Measure