The Effect of Non-Accounting Information on Equity Valuation Model

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The Effect of Non-Accounting Information on Equity Valuation Model

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. me i l u n @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 ISBN : 978-0-9742114-7-3

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. Francis and

Schipper (1999) think the value relevance exists in the statistical relationship between accounting

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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 valuation. Details of the building process of our model follow.

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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 summation of those of the three departments:

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L  L  L  L R M P (2) K  K R  K M  K P

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:

At Ct  1 R (4) Bt Ct  1 M

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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:

F H  F H  1 E E K K (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  et dL L dK K  L L K (6)   R R Y  R dR R  M M Y  M dM M 

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, 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

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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):

  Pt  (R f ) Et [dt ] ) (7)  1 where

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Pt = the market value, or price, of the firm’s equity at date t.

dt = net dividends paid at date t.

Rf = the risk-free rate plus one.

Et [.] = 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:

  ~ a Pt  yt  (R f ) Et [xt ] (9)  1

~ a xt  xt  (R f 1)yt1 (10)

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where

a xt = 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)

~ a is to provide the association between current information and future abnormal earnings. Assume {x } 1 satisfies the stochastic process:

~ a a ~ xt1  xt  vt  1t1 (11)

~ ~ vt1   vt   2t1 (12)

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.

a Pt  yt 1 xt  2vt (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  et dL L dK K  L L K (14)   R R Y  R dR R  M M Y  M dM M 

Substitute GTFP (14) for non-accounting information vt into (13), yields the following relationship for market value among book value, abnormal earnings, and GTFP:

a Pt   0 1 yt  2 xt  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  et dL L dK K  L L K (16)   R R Y  R dR R  M M Y  M dM M  

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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.

Next, we conduct an experiment to investigate the relationship for stock price with book value,

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earning per share, and GTFP. We modify Ohlson's linear equity model to the following:

a Pt   0 1 yt  2 xt  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 administration activity. We can see from Table 1 that the expense of R&D is increasing during the

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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

the non-accounting information (vt ) of Ohlson’s model for establishing the relationship with the market

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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.

The coefficients of scales of R&D expense, and marketing and administration are significantly

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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 relationship with the market value.

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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 non- accounting information in Ohlson's model.

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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 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.

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17. Sougiannis, T., 1994, “The Accounting Based Valuation of Corporate R&D,” The Accounting

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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.

June 22-24, 2008 25 Oxford, UK 2008 Oxford Business &Economics Conference Program ISBN : 978-0-9742114-7-3

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 1999 mean 7,912,693 1,027 11,581,779 262,461 466,597 std (13,437,145 ) (1,845 ) (23,357,459 ) (522,980 ) (658,684 ) 2000 mean 11,771,129 1,250 16,585,431 376,683 376,683 std (22,779,905) (2,316 ) (41,212,206 ) (981,606 ) (981,606 ) 2001 mean 10,776,279 1,117 17,375,878 483,722 629,893 std (21,451,051 ) (1,752 ) (41,595,508 ) (1,374,650 ) (1,092,147 ) 2002 mean 13,747,456 1,213 19,434,984 512,129 659,968 std (30,609,845 ) (1,931 ) (44,051,334 ) (1,326,279 ) (1,042,817 ) 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 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.

June 22-24, 2008 26 Oxford, UK 2008 Oxford Business &Economics Conference Program ISBN : 978-0-9742114-7-3

Table 2: Descriptive statistics – Empirical variables (Numbers are in percentage) dY/Y dL/L dK/K dR/R dM/M R/Y M/Y Mean 11.71 3.26 16.75 14.07 8.96 4.67 9.39 Median 13.65 4.37 12.34 14.87 9.99 2.70 7.49 Std. Dev. 34.16 29.67 27.80 37.33 32.36 6.21 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.

June 22-24, 2008 27 Oxford, UK 2008 Oxford Business &Economics Conference Program ISBN : 978-0-9742114-7-3

Table 3: Productivity analysis model – Empirical results

dY Y  et  et dL L dK K  L L K (16)   R R Y  R dR R  M M Y  M dM M  

Coefficient 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.

June 22-24, 2008 28 Oxford, UK 2008 Oxford Business &Economics Conference Program ISBN : 978-0-9742114-7-3

Table 4: Equity valuation model – Empirical results

a Pt   0 1 yt  2 xt  3GTFPt   (17)

Coefficient Estimates 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, xt 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.

June 22-24, 2008 29 Oxford, UK

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