2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
The Effect of Non-Accounting Information on Equity Valuation Model
Mei-Lun Lin
Tainan University of Technology, Taiwan, R.O.C.
886-6-2535649
lin.meilun@gmail.com
Chung-Jen Fu
National Yunlin University of Science & Technology, Taiwan, R.O.C.
June 22-24, 2008
Oxford, UK
2008 Oxford Business &Economics Conference Program
June 22-24, 2008
Oxford, UK
ISBN : 978-0-9742114-7-3
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2008 Oxford Business &Economics Conference Program
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The Effect of Non-Accounting Information on Equity Valuation Model
ABSTRACT
In this paper we derived the GTFP from output growth model as a proxy for non-accounting
information of Ohlson’s linear equity valuation model. GTFP exhibits a similar concept of
non-accounting information, produces realistic and robust results in practice, and highly correlates with
equity valuation. The data used in this study is cross-sectional and of a time-series. Hence, we use Panel
data model to analyze empirical results to overcome the problems of autocorrelation and
heteroskedasticity due to time-series analysis and cross-sectional analysis, respectively. The empirical
results show that the coefficients of book value, abnormal earnings, and GTFP in Ohlson's model for
equity valuation are significantly positive. This study integrates theoretical models and provides
empirical results for validation. It provides directions for a firm to improve long-term performance for
equity valuation.
Keywords: Equity valuation model, output growth model, non-accounting information, GTFP
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INTRODUCTION
A linear accounting-base valuation model that derived from the Ohlson (1995) and Feltham and
Ohlson (1995) research work shows that anticipated financial outcomes depend on two kinds of
information: First, current accounting data, namely, abnormal earnings, book value, and second,
non-accounting information. It is a pioneer work which formally integrates the analysis of market with
accounting and non-accounting data. Many researches have followed this model. However, most of their
work focuses on accounting data for equity valuation. The lack of analysis of non-accounting data may
defeat the effectiveness of the Ohlson’s model since the current accounting data cannot fully accounts
for future earnings. Therefore, this paper proposes to use GTFP as a proxy for the non-accounting
information to further analyze equity valuation.
In the competitive environment, increasing corporate performance has always been the focus
among industries, governments, and academia. Sougiannis (1994) investigates the long-term impact of
R&D for earnings and market value based on the accounting based valuation. Lev & Sougiannis (1996)
analyze the impacts of R&D for various industries. Their results show that there is a certain impact
contributed by R&D on corporate earnings. Ohlson (1995) derives a linear equity valuation model based
on the assumptions that the market value of the firm equates to the present value of expected dividends
(PVED), clean surplus relation (CSR), and linear dynamic model (LDM). Feltham and Ohlson (1995)
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suggest that, under the assumption of linear information model (LIM), one can use the current book
value and abnormal earnings for equity valuation. Most of the studies focus on the relationship for
market value with earnings and book value (Collins et al, 1997; Francis and Schipper, 1999; Lev and
Zarowin, 1999). The analysis of non-accounting information is rare (Amir and Lev, 1996). Chang et
al.(2002) and Yang et al.(2001a, b) use neoclassical production function1 to estimate GTFP of Solow
residual. However, their work does not consider the integration of equity valuation. This paper integrates
Ohlson's linear equity valuation model (1995) and Yang et al. (2001a,b) output growth model for
electronic firms to construct the non-accounting information of Ohlson's model, using a sample drawn
from Taiwanese electronic firms.
The remainder of this paper is organized as follows. In Section II, we develop a productivity
analysis model that combines the contribution of the departments of production, R&D, and marketing
and administration. We then provide an estimate for the non-accounting information in Ohlson's model.
This theoretical framework is further refined into a testable form, Section III describes sample selection
and research design. Results of the empirical tests are presented in Section IV. The final Section is
conclusion.
1
Neoclassical production function satisfies Inada condition (Inada, 1963) and is characterized by positive and decreasing
marginal productivity as well as constant returns to scale. Via Euler equation, the output of R&D department concerning in
this paper can be represented by the expenditure in R&D.
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THE MODEL
One of the hottest discussion topics in the world is upgrading industries. Not only does it affect the
survival and growth of an industry, but also the continuing growth and prosperity of an economy. The
development of science and technologies promotes the development of an economy. Hence, it is an
important factor for the development of a whole country. It also determines the prosperity of a country
and the progress of a society. In a fast changing environment, to modernize a country, besides other
efforts, highly depends on the development of science and technologies. If a government can establish
effective policies to encourage industries to invest in R&D, it will benefit industries to upgrade rapidly.
Thus, it will increase a competitive edge of a country and, therefore, become a main stream in the world.
Ball and Brown (1968) provide an empirical evaluation of accounting income numbers. Their work
leads the research in capital market analyses. However, the theory provided by Ohlson (1995) and
Feltham and Ohlson (1995) reestablish the relationship between accounting information and equity
valuation (Bernard, 1995). Barth, Beaver and Landsman (2001) point out that if accounting numbers
exhibit the correlation of market value, then the numbers are deemed to be value relevance.
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Francis
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and Schipper (1999) think the value relevance exists in the statistical relationship between accounting
information and market value or earnings.
Since Ball and Brown (1968) propose that market value is the present value of expected dividends,
many researchers focus on building theories, discussing econometric methods, and analyzing changes of
research designs. Nonetheless, the empirical results show little relevance between earnings and market
values (Lev, 1989). Lev thinks it is due to the poor quality of accounting information. On the contrary,
Ohlson(1995) and Feltham and Ohlson (1995) suggest that, under simple assumptions, the market value
can be valuated by earnings and book value.
To construct non-accounting information in Ohlson's model, we use a sample drawn from
Taiwanese electronic firms. The test sample is compiled from firms in the electronics industry listed in
the Taiwan Stock Exchange or Taiwan Over-the-Counter. Our model integrates Ohlson's linear equity
valuation model (1995) and Yang et al. (2001a,b) output growth model for electronic firms to construct
the non-accounting information.
Our model for equity valuation is formalized as follows. First, we establish a productivity analysis
model. Second, based on Solow residual method, we derive an estimate for GTFP as a proxy for the
non-accounting information in Ohlson's model in order to investigate its relationship with equity
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valuation. Details of the building process of our model follow.
Productivity analysis model
According to Kaplan and Atkinson (1999), a firm's value creation process can be divided into six
parts: R&D, design, production, marketing, logistics, and after-sale service. Following Yang, Fu, and
Chang (2001a,b), we combine R&D and design, the two parts prior to production, into a R&D activity.
We also merge marketing, logistics, and after-sale service, the three parts after production, into a
marketing and administration activity. Thus a firm’s activities can be divided into the activities of R&D,
production, and marketing and administration.
The firm’s total output (Y), therefore, can be defined as the summation of these three departments’
outputs:
Y  RM P
(1)
R, M, and P are the outputs of departments of R&D, marketing and administration, and production,
respectively.
Each department needs the inputs of labor (L) and capital (K). The total labor and capital are the
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summation of those of the three departments:
L  L R  LM  L P
(2)
K  KR  KM  KP
In order to increase performance and productivity, the high-technology electronic industry needs to
focus on the improvement of technology and the increase of efficiency introduced by R&D. For
simplicity of the analysis, we assume that the technological progress is an exponential function with a
constant change rate, and, based on the steady-state condition, we assume the technology change is
categorized as labor-augmenting. The productivity of a department is a function of neoclassical
productivity function:
R  F  At LR , K R 
M  G Bt LM , K M 
(3)
P  H C t LP , K P 
where A(t), B(t), and C(t) are the technology change factors for the departments of R&D, marketing and
administration, and production, respectively. A(t)LR, B(t)LM, and C(t)LP are the effective labor for the
three departments, accordingly.
Additionally, the technology change factors for each and every department may be different. Hence,
we assume the relative ratios of technology change factors for the three departments are:
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At  C t   1   R
(4)
Bt  C t   1   M
where  i i  R, M  is an unknown constant variable, usually,  i ＞-1.
According to the principle of optimal resource allocation, the optimal investment on labor and
capital requires the equal marginal output values among departments.
Therefore, the relative values for
departmental marginal productivity are shown as:
FE H E  FK H K  1
(5)
GE H E  GK H K  1
where FE  R  At LR  , GE  M Bt LM  , and H E  P Ct LP  represent the effective
marginal labor productivity for departments R&D, marketing and administration, and production,
respectively. FK, GK, and HK are the marginal capital productivity for the respective departments.
Using equations (2) - (5), the total differential of equation (1) yields our productivity model:


dY Y  e t L   e t L dL L    K dK K 
  R R Y    R dR R    M M Y    M dM M 
(6)
where  L  H E L Y  ,  K  H K K Y  ,  R  H R R Y  ,  M  H M M Y  ,  R  1  1 (1   R ) ,
and  M  1  1 (1   M ) .
As discussed before, output growth is attributable to input factor growth and productivity growth,
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and the latter can be further decomposed into technological change and efficiency change. The variable
dL / L indicates the growth rate of labor; dK / K is the growth rate of capital; and dM / M is the
growth rate of marketing and administration.  depicts technological change rate. As indicated in the
Appendix, we assume technological change is an exponential function, e t , with a constant change rate
 . Efficiency change is caused by R&D. The short-term and long-term impacts of efficiency change of
R&D are described by dR / Y and R / Y , respectively. The short-term and long-term impacts of
efficiency change for marketing and administration are described by dM / Y and M / Y , respectively.
Construction of non-accounting information in Ohlson's model
One of the most fundamental and common methods for equity valuation is the present value of
expected dividends from the traditional financial theory. However, it is impractical to use due to the fact
that it is difficult to estimate expected dividends. Ohlson (1995) derives a linear equity valuation model
via PVED, CSR, and LDM. In this model, anticipated financial outcomes depend on two kinds of
information: First, current accounting data, namely, earnings, book value, and second, non-accounting
information. Consider an economy with risk neutrality and homogenous beliefs. Basic assumptions are,
first, the market value of the firm equates to the present value of expected dividends (PVED):
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
Pt   ( R f )  Et [d t  ] )
(7)
 1
where
Pt = the market value, or price, of the firm’s equity at date t.
d t = net dividends paid at date t.
Rf = the risk-free rate plus one.
E t [.] = the expected value operator conditioned on the date t information.
Second, the clean surplus relation (CSR) applies:
yt 1  yt  dt  xt
(8)
where
yt = book value at date t.
xt = earning at date t.
With these two assumptions one obtains the well-known valuation formula:
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
Pt  y t   ( R f )  Et [ ~
xta ]
(9)
~
xta  xt  ( R f  1) yt 1
(10)
 1
where
xta = abnormal earnings at date t.
Additionally, Ohlson adds linear time series equations to specify the stochastic process between
abnormal earnings and non-accounting information. The importance of the linear dynamic model (LDM)
is to provide the association between current information and future abnormal earnings. Assume
{~
xa } 1 satisfies the stochastic process:
~
xta1  xta  vt  ~1t 1
(11)
 vt  ~2t 1
(12)
v~t 1 
Combing PVED, CSR, and LDM yields the following linear equity valuation model. Adding book
value to the abnormal earning and non-accounting information ( vt ) useful for forecasting abnormal
earnings determines market value.
Pt  y t   1 xta   2 vt
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(13)
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Yang et al.(2000a, b) constructs the following output growth model, calculated based on Solow
residual method:


GTFP  dY Y  e t L   e t L dL L    K dK K 
  R R Y    R dR R    M M Y    M dM M 
(14)
Substitute GTFP (14) for non-accounting information vt into (13), yields the following relationship
for market value among book value, abnormal earnings, and GTFP:
Pt   0   1 y t   2 xta   3GTFPt
(15)
In the next section, we will employ equations (6) and (15) in the empirical analysis to understand
whether the theoretical linkage between output growth and various factors is consistent with the
empirical data.
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RESEARCH DESIGN
Sample selection
To construct non-accounting information in Ohlson's model, we use a sample drawn from
Taiwanese electronic firms. The test sample is compiled from firms in the electronics industry listed in
the Taiwan Stock Exchange or Taiwan Over-the-Counter.
We use Panel data (Greene, 2001) in the
analysis and the sample consists of 604 firm-year observations, drawn from 151 firms, over the period of
1999-2002. All the data needed are collected from the financial and stock price database of Taiwan
Economic Journal (TEJ).
One of the selection criteria is that a firm must be listed in the stock exchange or exchanged
over-the-counter so that the stock prices are publicly available. Another criterion is that a firm must
invest in R&D. Therefore, a firm with zero R&D expenses is excluded from our sample. Concerning
risk-free rate, we precisely calculate the risk-free rate of each year from weighted average of one-year
time deposit interest rate of Bank of Taiwan by considering the interest fluctuation and the interest rate
interval. Thus, abnormal earnings per share is defined by earning per share minus beginning-of-period
book value multiplied by the risk-free rate.
The data used in this study is cross-sectional and of a time-series. Hence, we use Panel data model
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to analyze empirical results to overcome the problems of autocorrelation and heteroskedasticity due to
time-series analysis and cross-sectional analysis, respectively.
Approach to testing
The theoretical model developed in Section 2 indicates that the performance of a company is
affected by the inputs of labor and capital, technological changes, as well as the short-term and
long-term efficiency changes caused by R&D and marketing and administration expenditure. To
empirically test the model, we use the growth rate of sales revenue as performance measures. The
growth rate of sales revenue measures business growth. The quantity of labor input is measured by the
number of employees.
The quantity of capital input and R&D activity are measured by total assets and
R&D expenditures, respectively. The marketing and administration activity are measured by marketing
and administration expenditures.
We employ the panel data to do the empirical analysis.


dY Y  e t L   e t L dL L    K dK K 
  R R Y    R dR R    M M Y    M dM M   
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(16)
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where
dY/Y:
corporate performance, measured by the growth rate of sales revenue. The growth rate of
revenue is calculated by the natural logarithm of net sales revenue less the natural
logarithm of net sales revenue in the previous year.
dL/L:
The growth rate of the number of employees, calculated by the natural logarithm of the
number of employees less the natural logarithm of the number of employees in the
previous year.
dK/K:
The growth rate of total assets, assessed by the natural logarithm of total assets less the
natural logarithm of total assets in the previous year.
dR/R:
growth rate of R&D expenditure, proxied by the growth rate of R&D expenses for the
previous period , calculated by the natural logarithm of the R&D expenditure less the
natural logarithm of the R&D expenditure in the previous year.
dM/M:
growth rate of marketing and administration expenditure, proxied by the growth rate of
marketing and administration expenses, assessed by the natural logarithm of the
marketing and administration expenditure less the natural logarithm of the marketing and
administration expenditure in the previous year.
R/Y:
scale of R&D expenditure, proxied by the ratio of R&D expenses over sales revenue.
M/Y:
scale of marketing and administration, proxied by the ratio of marketing and
administration expenses over sales revenue.
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Next, we conduct an experiment to investigate the relationship for stock price with book value,
earning per share, and GTFP. We modify Ohlson's linear equity model to the following:
Pt   0   1 y t   2 xta   3GTFPt  
(17)
where  0 an intercept term, and the rest of the variables are as defined above. We expect to see
 1 ,  2 , and  3 positive.
EMPIRICAL RESULTS
Descriptive statistics
Table 1 lists the mean and standard deviation of descriptive variables for our sample firms. The
standard deviations of sales revenue, the number of employees, total assets, R&D expenditures,
marketing and administration are larger than the respective means, showing a large variation of firm size
for the sample firms. The average R&D expense exceeds 400 million, reflecting this industry’s
substantial efforts into R&D. However, the average expenses on marketing and administration are even
larger than R&D expenses, showing these firms’ management strongly attend to marketing and
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administration activity. We can see from Table 1 that the expense of R&D is increasing during the
observation period, from 262 million in 1999 to 512 million in 2002, with a highly increasing rate at
95%. Hence, the sample firms very much emphasize the need for R&D.
The descriptive statistics of the variables used in the model are presented in Table 2. The average
growth rate of total output is 11.71%. The growth rate of labor input is the lowest, on average, 3.26%.
Capital input grows 16.75%, the highest, revealing the sample firms have actively expanded their
production capacity. The average growth rate of R&D expenses is also rather high, 14.07%, but there is
much variation, indicating a huge difference among the sample firms. As for marketing and
administration expenditure, it grows 8.96%. The average scales of R&D and marketing and
administration expenses are 4.67% and 9.39%, respectively.
Empirical results
The empirical results can be categorized into two parts. Fist, based on the estimate of variables used
in the productivity analysis model, we derive productivity.
Second, using Equation (14), calculated
based on Solow residual method, we can calculate GTFP by subtracting the growth rate of leading
factors from the growth rate of sales revenue. This is to verify whether GTFP can be used as a proxy for
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the non-accounting information (vt ) of Ohlson’s model for establishing the relationship with the market
value.
Productivity analysis model
Table 3 lists the estimates of variables derived by using Panel data model for the Taiwanese
electronic firms during the period 1999-2002. Except the growth rate of labor input is insignificant, the
others are shown to be significant. This is possibly due to the fact that the number of employees grows
slowly during the observation period. The technological change rate  in Equation (16) is shown to
be significant as well.
The coefficients of the growth rates of capital input, R&D expense, and marketing and
administration expense, represented by  K ,  R , and  M , respectively, are all significantly positive.
This indicates it has a positive impact on the corporate performance. Especially, the coefficient of
growth rate of capital input ( K  0.418) indeed indicates the growth of capital input brings a positive
impact on the sales revenue. In general, capital input is an important indicator of a firm’s production
capability, evaluated by production capacity and production technology, and can even be a key to future
competitive edge.
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The coefficients of scales of R&D expense, and marketing and administration are significantly
negative. It suggests that due to the uncertain effect by R&D as well as the reporting of R&D as an
expense by the current regulation of Statements of Financial Accounting Standards, it will adversely
affect the performance of the current accounting year. However, the positive impact generated by the
growth rate of R&D expenses, as explained in the previous paragraph, gradually shows increasing sales
revenue. A firm should not simply focus on the current earning. Instead, a firm should continue investing
in R&D for its positive impact on the corporate performance.
Empirical analysis for the equity valuation model
Based on the estimate of GTFP, calculated by Solow residual method, as a proxy for the
non-accounting information of Ohlson’s model, we evaluate the relationship between GTFP and the
market value. Table 4 shows the estimates of variables, used in the equity valuation model, analyzed by
using Panel data model, for the Taiwanese electronic firms during the period of 1999-2002 with
significance. The significantly positive coefficients of performance indicators abnormal earnings and
GTFP (  2  3.351 ,  3  4.911 ) show a consistent forecast with the one predicted by the model. It
suggests that accounting data such as earnings and non-accounting data GTFP exhibit a positive
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relationship with the market value.
The use of GTFP as a proxy for the non-accounting information for long-term impacts on market
value analyses is strongly supported by the evidence of its coefficient shown to be positive and
significant. It also shows that GTFP can be used as a proxy for the non-accounting information of
Ohlson’s model.
CONCLUSION
This paper integrates Ohlson’s linear equity valuation model (1995) and Yang et al. al. (2001a,b)
output growth model for electronic firms to construct the non-accounting information of Ohlson's model,
using a sample drawn from Taiwanese electronic firms reporting during the years 1999-2002. The data
used in this study is cross-sectional and of a time-series. Hence, we use Panel data model to analyze
empirical results to overcome the problems of autocorrelation and heteroskedasticity due to time-series
analysis and cross-sectional analysis, respectively. The empirical results show that the coefficients of
book value, abnormal earnings, and GTFP in Ohlson's model for equity valuation are significantly
positive. It fits in with the expectation of theory Thus, GTFP can be used as a proxy for the
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non-accounting information in Ohlson's model.
This study integrates theoretical models and provides empirical results for validation. It provides
directions for a firm to improve long-term performance for equity valuation. The major contributions of
this study are threefold. First, from an academia research perspective, since the data used in this study is
cross-sectional and of a time-series, we use Panel data model to analyze empirical results to overcome
the problems of autocorrelation and heteroskedasticity. The research method dramatically differs from
regression analysis methods, which are commonly used by other researchers. In addition, we propose to
use GTFP as a proxy for non-accounting information of Ohlson’s model. GTFP exhibits a similar
concept of non-accounting information, produces realistic and robust results in practice, and highly
correlates with equity valuation.
Second, from the firms’ perspective, this study helps them to realize whether investment in R&D
generates sales revenue. Therefore, for those firms with insufficient R&D investment, they must
consider whether or not to adjust their R&D strategies to improve corporate performance. Additionally,
this study helps them to evaluate the impacts of R&D investment.
Third, from the perspective of government public policies, this study can benefit the government as
a reference for incentive policies to promote R&D. It will encourage industries to invest in R&D to
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increase their competitive capacities, and it is of an ultimate importance to increase a competitive edge
of a country to establish incentive policies to promote R&D.
REFERENCE
1. Amir, E., and B. Lev, 1996, “Value-relevance of nonfinancial information: The wireless
communications industry,” Journal of Accounting & Economics, Vol. 22, p3-30.
2. Ball, R., and P. Brown, 1968, “An Empirical Evaluation of Accounting Income Numbers,” Journal
of Accounting Research 6, pp.159-178.
3. Barro, R. J. and X. Sala-I-Martin, 1995, Economy Growth. New York: McGraw- Hill.
4. Barth, M. E., W. Beaver and W. R. Landsman, 2001, “The relevance of the value-relevance literature
for financial accounting standard setting: Another view,” Journal of Accounting & Economics, Vol.
31 Issue 1-3, p77-103.
5. Bernard, V. L., 1995, “The Feltham-Ohlson Framework: Implications for Empiricists,”
Contemporary Accounting Research 11, pp733-747.
6. Chang, B. G, C. C. Yang, and H. Y. Lin, 2002, “The Determinants of Operating Performance for US
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Electronic Firms,” AAA 2002 Annual Meeting.
7. Collins, D. W., E. L. Maydew and
I. S. Weiss, 1997, “Changes in the value-relevance of earnings
and book values over the past forty years,” Journal of Accounting & Economics, Vol. 24 Issue 1,
p39-67.
8. Feltham, G. A., and J. A. Ohlson, 1995, “Valuation and Clean Surplus Accounting for Operating and
Financial Activities,” Contemporary Accounting Research, 11, 689-731.
9. Francis, J.,and K. Schipper, 1999, “Have financial statements lost their relevance? Journal of
Accounting Research, Vol. 37 Issue 2, p319-352.
10. Greene, W. H., 2001. Econometric Analysis. Fourth Edition, Prentice Hall.
11. Kaplan, R. S., and A. A. Atkinson. 1998. Advanced Management Accounting. Third Edition, Prentice
Hall.
12. Lev, B., 1989, “On the Usefulness of Earnings and Earnings Research: Lessons and Directions from
two Decades of Accounting Research,” Journal of Accounting Research, 27, Supplement, 153-192.
13. Lev, B., and P. Zarowin, 1999, “The boundaries of financial reporting and how to extend them,”
Journal of Accounting Research, Vol. 37 Issue 2, p353-389.
14. Lev, B., and T. Sougiannis, 1996, “The Capitalization, Amortization, and Value-Relevance of R&D,”
Journal of Accounting and Economics, 21, 107-138.
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15. Ohlson, J.A., 1995, “Earnings, Book Values, and Dividends in Equity Valuation,” Contemporary
Accounting Research, 11, 661-687.
16. Solow, R. M., 1969, Investment and Technical Change, in Kenneth J. Arrow, et al., eds.,
Mathematical Method in the Social Sciences, Pal Alto, Stanford University Press.
17. Sougiannis, T., 1994, “The Accounting Based Valuation of Corporate R&D,” The Accounting Review,
69(1), 44-68.
18. Yang, C. C., C. J. Fu, and B. G. Chang, 2001a, “The Determinants of Operating Performance for the
Electronic Firms in Taiwan,” Taiwan Economic Association Annual Conference Proceedings, 55-80.
19. Yang, C. C., S. J. You, and C. J Fu, 2001b, “The Impacts of R&D on the Performance of
High-Technology Industry in Taiwan,” Journal of Technology Management, 55-69.
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Table 1: Mean and standard deviation
(TR, TA, RD, and ME are in thousand dollars; NL is the number of employees.)
TR
NL
TA
RD
ME
7,912,693
1,027
11,581,779
262,461
466,597
(13,437,145 )
(1,845 )
11,771,129
1,250
(22,779,905)
(2,316 )
10,776,279
1,117
(21,451,051 )
(1,752 )
13,747,456
1,213
(30,609,845 )
(1,931 )
1999- mean
11,051,889
1,152
16,244,518
408,749
585,507
2002 std
22,934,165
1,970
38,466,441
1,106,823
960,804
1999 mean
std
2000 mean
std
2001 mean
std
2002 mean
std
(23,357,459 ) (522,980 )
16,585,431
376,683
(41,212,206 ) (981,606 )
17,375,878
483,722
(658,684 )
376,683
(981,606 )
629,893
(41,595,508 ) (1,374,650 ) (1,092,147 )
19,434,984
512,129
659,968
(44,051,334 ) (1,326,279 ) (1,042,817 )
NOTE: Numbers in parenthesis are standard deviations. TR is sales revenue; NL is the number of
employees, TA denotes total assets; RD represents R&D expenses; ME is marketing and
administration expenses. The sample consists of 604 firm-year observations, drawn from 151
firms.
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2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Table 2: Descriptive statistics – Empirical variables
(Numbers are in percentage)
Mean
Median
Std. Dev.
dY/Y
dL/L
dK/K
dR/R
dM/M
R/Y
M/Y
11.71
13.65
34.16
3.26
4.37
29.67
16.75
12.34
27.80
14.07
14.87
37.33
8.96
9.99
32.36
4.67
2.70
6.21
9.39
7.49
7.47
NOTE: dY/Y denotes the growth rate of sales revenues; dL/L is the growth rate of the number of
employees; dK/K represents the growth rate of total assets; dR/R is the growth rate of R&D
expense; dM/M denotes the growth rate of marketing and administration expense; R/Y represents
the ratio of R&D expenses over sales revenue; M/Y is the ratio of marketing and administration
over sales revenue. N=604.
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2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Table 3: Productivity analysis model – Empirical results


dY Y  e t L   e t L dL L    K dK K 
  R R Y    R dR R    M M Y    M dM M   
Coefficient
(16)
Estimates
t value
P value

0.354
10.150
0.000***
L
0.063
1.360
0.173
K
0.418
8.020
0.000***
R
0.106
3.050
0.002**
M
0.249
6.240
0.000***
R
-2.945
-6.420
0.000***
M
-2.228
-7.230
0.000***
F test
Prob>F= 0.0000***
R 2 = 0.3522
NOTE: 1. ***
and ** designate statistical significance at the levels of 0.001 and 0.10,
respectively.
2. dY/Y denotes the growth rate of sales revenues; dL/L is the growth rate of the number of
employees; dK/K represents the growth rate of total assets; dR/R is the growth rate of R&D
expense; dM/M denotes the growth rate of marketing and administration expense; R/Y
represents the ratio of R&D expenses over sales revenue; M/Y is the ratio of marketing and
administration over sales revenue. N=604.
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2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Table 4: Equity valuation model – Empirical results
Pt   0   1 y t   2 xta   3GTFPt  
Coefficient
Estimates
(17)
t value
P value
0
12.809
3.830
0.000***
1
0.702
3.700
0.000***
2
3.351
7.230
0.000***
3
4.911
1.800
0.072**
F test
Prob>F= 0.0000***
R 2 = 0.2873
NOTE: 1. ***
and ** designate statistical significance at the levels of 0.001 and 0.10,
respectively.
a
2. P is the market value, y is the book value, x t is abnormal earnings, defined by earnings per
share minus beginning-of-period book value multiplied by the risk-free rate. GTFP is the
growth of total factor productivity. N=604.
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