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A comparison of agency problems in Taiwanese high tech and traditional corporations.

ABSTRACT

The main objectives of the paper are to estimate the effect of ownership structure on a corporation's profitability and compare agency problems between high tech and traditional corporations. The study included four steps: descriptive statistics, multiple regression analysis, multiple regression analysis by size, and tests for homoscedasticity to refine our estimates. The results indicate that 1) traditional corporations have significant agency problems while high tech corporations have insignificant agency problems; 2) revenue growth rate was significantly associated with profitability for both large and small high tech corporations; and 3) revenue growth rate was significantly associated with profitability for small traditional corporations, but not for large traditional corporations.

INTRODUCTION

As the stock of many corporations is widely held by the public and managers of corporations have a great deal of autonomy, a potential conflict (agency problem) between managers (agents) and stockholders (principals) arises whenever a management team is not trying to maximize shareholders' wealth. "For example, it has been argued that managers of a large, well-entrenched corporation could work just hard enough to keep stockholder returns at a reasonable level and then devote the remainder of their efforts and resources to public service activities, to employee benefits, to higher executive salaries, or to golf (Brigham, 1995)."

Theoretically, the lack of entrepreneurship, unwillingness to take risks, short-term oriented decision-making, and the conflict between personal interests and the corporation's are some of the symptoms of agency problems. However, the separation of ownership and control of corporations will continue its trend due to the fact that managing a corporation requires experience, training, expertise, and a tremendous amount of capital in order for the corporation to survive, to realize economies of scale, and to compete globally.

Many empirical studies have evaluated the effect of separation of ownership and control on corporations' profitability. Two approaches were generally adopted based on theorems developed by Berle and Means (Berle, Means, 1932,1968):

1. Compare profitability of owner-operated corporations directly to that of manager-operated corporations (Kamerschen, 1968, Monsen et al., 1968), and/or

2. Study the impact of ownership's structure on corporations' profitability, as ownership structure could be a continuous variable in a cross-sectional dataset.

The findings are not only inconsistent but also inconclusive:

1) Percentage owned by the management group and profitability have a positive linear relationship (Hudson et al, 1992, Jensen & Meckling 1976, Niehaus 1989).

2) Percentage owned by the management group and profitability have a non-linear relationship (McConnell & SERVAES 1990, Morck & Vishny 1988, Stulz 1988).

3) Percentage owned by the management group and profitability have a positive (negative) relationship if corporations were small or medium (large) (Monsen et al 1968).

4) Ownership structure with higher concentration ratio could have positive, negative, or no effect on profitability (Admati & Zechner 1994, Hindley 1970, Leech & Leahy 1991, Neun 1986, Prowse 1992).5) There was no relationship between executive stock options and corporations' profitability (Loderer & Martin 1997).

All previous studies were based on cross-sectional datasets, which included corporations from industries of all categories. No study has compared the impact of ownership's structure on corporations' profitability among industries. As variation among industries could be quite significant, e.g. the characteristics of high tech corporations may well be totally different from those of traditional corporations, the agency problem of high tech corporations could behave differently than traditional corporations. To examine this issue, this study classifies industries into two categories: 1) High tech corporations which have higher revenue growth rate (41.48%) and higher profitability (4.81%); and 2) Traditional corporations which are deemed mature with an average revenue growth rate of 9.06% and lower profitability (1.96%).

The main objectives of the paper are to: 1) Estimate the effect of ownership structure on corporations' profitability of high tech and traditional corporations respectively; 2) Compare agency problems between high tech and traditional corporations; and 3) Provide implications for stockholders' wealth maximization.

METHOD OF ANALYSIS

Model specification

As the main feature of the paper is to estimate the agency problem of high tech and traditional corporations, our model specification is consistent with many previous studies:

Model 1 : [Y.sub.i] = [a.sub.0] + [a.sub.1][X.sub.1i] + [a.sub.3][X.sub.3 i] + [[member of].sub.i], i=1,2,...,n

Model 2 : [Y.sub.i] = [b.sub.0] + [b.sub.2][X.sub.2i] + [b.sub.3][X.sub.3i] + [[member of].sub.i], i=1,2,...,n

Where,

Y= Return on total asset

[X.sub.1]= Percentage of stocks owned by management

[X.sub.2]= Concentration ratio of the largest five stockholders

[X.sub.3]= Revenue growth rate.

In the models specified above,

Return on total asset (ROA) (Y) is to approximate corporations' overall profitability due to the fact that ROA is a comprehensive measure reflecting corporations' efficient use of asset;

1. Percentage of stocks owned by management ([X.sub.1]) indicates the level of potentialagency problems;

2. Concentration ratio of the largest five stockholders ([X.sub.2]) indicates ownership structure; and

3. Revenue growth rate ([X.sub.3]) is a key control variable to remove variations of profitability caused by other factors across corporations.

Models of many previous studies (Hindley 1970, Jensen & Meckling 1976, Monsel el al 1968) incorporated both percentage of stocks owned by management ([X.sub.1]) and concentration ratio of the largest five stockholders ([X.sub.2]) into the same regression equation. If [X.sub.1] and [X.sub.2] are highly correlated, the regression analysis would not be able to separate the impact of [X.sub.1] on Y from that of [X.sub.2] on Y and would end up with insignificant t-ratios. In fact, our preliminary descriptive analysis indicates that the correlation coefficients between [X.sub.1] and [X.sub.2] were 0.92 and 0.82 for high tech and traditional corporations, respectively. Hence, our models only include either [X.sub.1] or [X.sub.2] and their effects on profitability are estimated separately.

Estimation procedure

The first step of the study estimates descriptive statistics of all variables to provide an overview of the database. The second (preliminary) step, estimates models 1 & 2 for both high tech and traditional corporations separately using multiple regression techniques. Due to the fact that average profitability of traditional corporations is very low (1.96%), multiple regression models without an intercept are applied for the estimation of traditional corporations in order to enhance independent variables' explanatory power on the dependent variable. As a result, four equations are estimated in step two.

In the third step of the study, we include a size factor. Due to the fact that a firm's size affects its concentration ratio and our sample size (degrees of freedom) allows us to differ the effect of small size corporations from that of large size corporations, we divided our data base further into two size categories with total assets of NT$10 billion ($312 million) as the dividing point. Hence, we estimate eight equations in step three.

In the final (fourth) step, we check for the potential problem of homoscedasticity as all models are estimated based on cross-sectional data. We apply the "two sample variance F-test" technique to test the hypothesis that the mean square error (MSE) of the large size corporations = the MSE of the small size corporations based on the results from step three. Pending the results, if the assumption of homoscedasticity has been violated, we have unbiased but inefficient estimates in step two and we would need to focus our interpretation on the results of step three. Otherwise, we would need to focus on the results of step two due to the relatively larger sample sizes.

Data collection and treatment

We collected returns on total asset (Y) from the financial database of Taiwan Economic Journal, with Y equals [after tax net income + interest expenses * (1- tax rate)] / (average total asset). Percentage of stocks owned by management ([X.sub.1]) was collected from corporations' accounting/auditing reports and [X.sub.1] equals (# of stocks owned by management) / (average total # of ordinary stocks outstanding). Concentration ratios of the largest five stockholders ([X.sub.2]) and revenue growth rates ([X.sub.3]) were collected from Quarterly Stock Market Summary reports, where [X.sub.2] equals (# of ordinary stocks owned by the five largest stockholders) / (average total # of ordinary stocks outstanding) and [X.sub.3] equals (total revenue of year t--total revenue of year t-1) / (total revenue of year t-1).

The database included a total of 399 public trading corporations in Taiwan in the year 2000. One hundred one corporations are classified as high tech corporations, including electronics, telecommunications, computer hardware, software, networking, information systems, etc. The rest (298) are traditional corporations such as clothing, textile, trading, agriculture, manufacturing, etc. Because the government regulates financial and insurance corporations differently, they are not included in the database.

THE RESULTS

Descriptive statistics

Table 1 presents a statistical summary of all variables for the regression models of the second step. The results indicate:

1. Returns of total asset (Y) of high tech corporations were significantly higher than that of traditional corporations;

2. Percentages of stocks owned by management ([X.sub.1]) and concentration ratios ([X.sub.2]) were comparable in both high tech and traditional corporations;

3. Percentages of stocks owned by management ([X.sub.1]) and concentration ratios ([X.sub.2]) were highly correlated with the same distribution pattern; and

4. High tech corporations' revenue growth rates were significantly higher than that of traditional corporations.

The results of Table 1 also imply that:

1. Agency problem of high tech corporations are significantly different from that of traditional firms, which confirms the validity and value of our initial assumption of the study; and

a, The multiple regression models should only include either [X.sub.1] or [X.sub.2], but not both, as they were highly correlated.

Table 2 presents descriptive statistics of all variables by corporations' size for the regression models of the third step. The results indicate that:

2. Small high tech corporations were less profitable than that of larger high tech corporations and small traditional corporations were comparable to larger traditional corporations' profitability;

3. Both small high tech and small traditional enterprises have higher percentages of stocks owned by management and concentration ratios; and

4. Both large high tech and large traditional corporations have higher revenue growth rates than their smaller peers.

As both the dependent and independent variables behave differently in terms of corporations' size, the estimation of step three (regression analysis by size) and the test in step four (test of homoscedasticity) should improve the results of step two (pooled multiple regression).

Regression results

Table 3 provides pooled multiple regression results of step two. The results indicate that:

1) Neither percentage of stocks owned by management ([X.sub.1]), nor concentration ratio of the largest five stockholders ([X.sub.2]) have any impact on profitability (Y) of high tech corporations;

2) Both percentage of stocks owned by management ([X.sub.1]) and concentration ratio of the largest five stockholders ([X.sub.2]) have significant and positive impact on profitability, (Y) of traditional corporations; and

3) Revenue growth rate ([X.sub.3]) has a significant and positive impact on profitability (Y) of high tech as well as traditional corporations.

Therefore, we can conclude from the results of step two that 1) agency problems of high tech corporations in Taiwan may not be tied to profitability; but 2) agency problems of traditional corporations in Taiwan may be associated with profitability; and 3) both high tech and traditional corporations would be more profitable if they were able to grow their revenue. More specifically, when traditional corporations have a higher percent of shares owned by their management, their profitability would also be higher.

Table 4 presents multiple regression results by corporations' size of step three. The results indicate that:

1. Neither the percentage of stocks owned by management ([X.sub.1]), nor the concentration ratio of the largest five stockholders ([X.sub.2]) have an impact on profitability (Y) of large or small high tech enterprises;

2. Both the percentage of stocks owned by management ([X.sub.1]) and the concentration ratio of the largest five stockholders ([X.sub.2]) have significant and positive impacts on profitability (Y) of large and small traditional corporations;

3. Revenue growth rate ([X.sub.3]) has a significant and positive impact on profitability (Y) of large and small high tech firms; and

4. Revenue growth rate ([X.sub.3]) has a significant impact on profitability (Y) of small traditional corporations, but not on large traditional corporations.

Therefore, we can conclude from the results of step three that 1) the agency problem of large and/or small high tech corporations in Taiwan may not be tied to its profitability; 2) the agency problem of large and/or small traditional firms in Taiwan may be associated with its profitability; 3) both large and small high tech companies would be more profitable if they were able to grow their revenue; and 4) small traditional corporations would be more profitable if they were able to grow their revenue, but not for large traditional enterprises.

By comparing results of step two to that of step three, we can conclude that both small and large high tech corporations behaved similarly while large traditional corporations' profitability have been contained with limited potentials.

Test of homoscedasticity

The final (fourth) step of the study, estimates the "two sample variance F-statistics" to test the hypothesis that the mean square error (MSE) of the large size corporations equals the MSE of the small size businesses based on the results of step three. The results, as shown in Table 5, indicate that residual values of small traditional corporations behave differently from that of large traditional firms. Hence, the multiple regression results for traditional corporations in step two might not be efficient estimates and the coefficients of revenue growth rate ([X.sub.3]) becomes insignificant for large traditional corporations in step three. Given the main objective of the study is to evaluate the impacts of ownership structure ([X.sub.1] and/or [X.sub.2]) on a firm's profitability (Y), and results from steps two and three were generally consistent, our conclusion is not affected at all.

IMPLICATIONS

The results indicate that traditional corporations have significant agency problems while high tech enterprises have insignificant agency problems. As high tech corporations' profitability depends more on their new product development/technology innovations while traditional businesses depend more on their managerial skills and efficiency, investors may need to include ownership structure of traditional corporations and technology leading edge of high tech firms as critical factors to consider for stockholder wealth maximization.

The results also indicate that revenue growth rate is insignificantly associated with profitability for large traditional companies. Since the market for traditional corporations is generally deemed mature, opportunities for revenue growth, especially for large traditional corporations, could be limited at the expense of lower profitability.

Since agency problems behaved differently between high tech and traditional corporations, the results imply that the effect of separation of ownership and control on today's corporations could be significant or insignificant, pending on the nature of the industry in which the corporation operates. Future studies of agency problems and stockholders' profit maximization may consider including variables to reflect leading edge/technology innovations of high tech corporations. For example, a direct comparison of product model years and features among corporations may help create measurement to approximate technology level.

SUMMARY AND CONCLUSION

The main objectives of the paper are to: 1) estimate the effect of ownership structure on corporations' profitability of high tech and traditional corporations respectively; 2) compare agency problems between high tech and traditional corporations; and 3) provide implications for stockholders' wealth maximization.

The results indicate that 1) traditional corporation have significant agency problems while the agency problems for high tech corporations are insignificant; 2) the pooled regression model for traditional corporations violates the assumption of homoscedasticity because the residual values of small traditional corporations behave differently from those of large traditional corporations; 3) revenue growth rate is significantly and positively associated with profitability for both large and small high tech corporations; and 4) revenue growth rate is significantly and positively associated with profitability for small traditional corporations, but not for large traditional corporations.

By comparing high tech corporations to traditional corporations, we also conclude that agency problems behave differently among industries, i.e. the effect of separation of ownership and control on today's firms could be significant or insignificant, pending on the nature of that industry. The study helps investors who seek stockholders' wealth maximization and future research needs to consider including variables to reflect technological factor in studying agency problems of high tech corporations.

REFERENCES

Admati, A., P. Pfleiderer & J. Zechner (1994). Large Shareholder Activism, Risk sharing and Financial Market Equilibrium, Journal of Political Economy, 102, 1097-130.

Berle, A & C. G. Means (1932). The Modern Corporate and Private property, New York: Macmillan.

Berle, A & C. G. Means (19680. The Modern Corporation and Private Property, New York: Harcourt, Brace & World, Inc., rev. ed.

Brigham, Eugene F (1995). Fundamentals of Financial Management, 7th edition, Orlando, Florida: Dryden Press.

Hindley, B. (19700. Separation of Ownership and Control in the Modern Corporation, The Journal of Law & Economics, 185-221.

Hudson, C. D., J. S. Jr. Jahera & W. P. Lloyd (1992). Further Evidence on the Relationship between Ownership and Performance, Financial Review, 27, 227-39.

Jensen, M. C. & W. H. Meckling (1976). Theory of the Firm Managerial Behavior, Agency Costs and Ownership Structure, Journal of Financial Economics, 3: 305-60.

Kamerschen, D. R. (1968). The Influence of Ownership and Control on Profit Rates, The American Economics Review, 60: 432-47.

Loderer, C. & K. Martin (1997). Executive Stock Ownership and Performance Tracking Faint Traces, Journal of Financial Economics, 45, 223-55.

Leech, D. & J. Leahy (1991). Ownership Structure, Control Type Classifications and the Performance of Large British Companies, The Economic Journal, 101, 1418-37.

McConnell, J. J. & H. Servaes (1990). Additional Evidence on Equity Ownership and Corporate Value, Journal of Financial Economics, 27: 595-612.

Monsen, R. J., J. S. Chiu & D. E. Cooley (19680. The Effect of Separation of Ownership and Control on the Performance of the Large Firms, Quarterly Journal of Economics, 82, 435-51.

Morck, R., A Shleifer & R. Vishny (1988). Management Ownership and Market Valuation: An Empirical Analysis, Journal of Financial Economics, 20, 293-315.

Neun, S. D. (1986). Dominant Stock Ownership and Profitability, Managerial and Decision Economics, 7, 207-210.

Niehaus, G. R. (1989). ownership Structure and Inventory Method Choice, Accounting Review, 64, 269-84.

Prowse, S. D. (1992). The Structure of Corporate Ownership in Japan, The Journal of Finance, XLVII, 3, 1121-40.

Shleifer, A. & R. Vishny (1986). Large Shareholders and Corporate Control, Journal of Political Economy, 94, 461-88.

Stulz, R. M. (19880. Managerial Control of Voting Rights: Financing Policies and the Market for Corporate Control, Journal of Industrial Economics, 22, 25-54.

Hsiao-Tien Pao, National Chiao Tung University

Tenpao Lee, Niagara University

Daniel L. Tompkins, Niagara University
Table 1: Statistical summary of all variables

 High tech corporations

Max. Min. Mean SE *

Return on total asset (Y)
 14.26 -5.69 4.81 3.52

Percentage of stocks owned by management ([X.sub.1])
 69.34 5.00 23.16 11.75

Concentration ratio of the largest five stockholders ([X.sub.2])
 56.38 4.13 22.73 11.73

Revenue growth rate ([X.sub.3])
281.60 -42.60 41.48 51.47

 Traditional corporations

Max. Min. Mean SE

Return on total asset (Y)
 15.54 -12.13 1.96 3.11

Percentage of stocks owned by management ([X.sub.1])
 79.77 2.12 23.48 15.03

Concentration ratio of the largest five stockholders ([X.sub.2])
 79.80 0.33 26.17 15.91

Revenue growth rate ([X.sub.3])
295.10 -100.60 9.06 39.06

* SE: Standard error.

Table 2: Mean values of all variables based on corporations' size

 High tech corporations Traditional corporations

Large size Small size Large size Small size

Average size in NT$ billions
38.05 5.08 32.60 4.79

Return on total asset (Y)
 5.14 4.89 2.05 2.04

Percentage of stocks owned by management ([X.sub.1])
20.16 25.57 21.65 24.05

Concentration ratio of the largest five stockholders ([X.sub.2])
19.51 25.20 23.00 27.34

Revenue growth rate ([X.sub.3])
48.33 37.06 12.06 7.55

Table 3: Pooled multiple regression results (step two)

Dependent variable: Return on total asset (Y)

Independent variables: Percentage of stocks owned by management
 ([X.sub.1])

 Concentration ratio of the largest five
 stockholders ([X.sub.2])

 Revenue growth rate ([X.sub.3])

Intercept [X.sub.1] [X.sub.2] [X.sub.3]

High tech corporations
 2.794 0.019 0.038
 (4.133) * (0.748) (6.561) *
 2.484 0.033 0.038
 (3.684) * (1.302) (6.655) *
Traditional corporations
 0.074 0.014
 (11.786) * (3.233) *
 0.065 0.014
 (11.257) * (3.164) *

Intercept R-square Root MSE Sample size

High tech corporations
 0.32 2.927 101
 0.328 2.911 101
Traditional corporations
 0.364 2.938 298
 0.346 2.98 298

Numbers in parentheses are t-ratios.

* Significant at 1% probability level

Table 4: Multiple regression results by size
(step three)

Dependent variable: Return on total asset (Y)
Independent variables:Percentage of stocks owned
by management ([X.sub.1])
Concentration ratio of the largest five
stockholders ([X.sub.2])
Revenue growth rate ([X.sub.3])

Intercept [X.sub.1] [X.sub.2] [X.sub.3]

Large high tech corporations

 2.980 0.021 0.032
 (3.731) * (0.615) (5.196) *
 2.658 0.038 0.032
 (3.343) * (1.124) (5.272) *

Small high tech corporations

 2.586 0.012 0.050
 (2.378) * (0.316) (4.518) *
 2.215 0.027 0.050
 (2.038) (0.719) (4.579) *

Large traditional corporations

 0.068 0.008
 (7.978) * (1.703)
 0.065 0.009
 (7.849) * (1.636)

Small traditional corporations

 0.076 0.018
 (9.259) * (2.894) *
 0.065 0.018
 (8.725) * (2.800) *

Intercept R-square Root MSE Sample size

Large high tech corporations

 0.424 2.508 43
 0.437 2.48 43

Small high tech corporations

 0.285 3.220 57
 0.291 3.207 57

Large traditional corporations

 0.448 2.193 100
 0.441 2.208 100

Small traditional corporations

 0.347 3.243 198
 0.323 3.300 198

Numbers in parentheses are t-ratios.

* Significant at 1% probability level

Table 5: F-test of homoscedasticity ([H.sub.o]: [MSE.sub.large]
= [MSE.sub.small])

 [F.sub.a] c (5% significant level)

Model 1:
 High tech corporations 1.648 1.690 (Fail to reject)
 Traditional corporations 2.187 1.430 (Reject)

Model 2
 High tech corporations 1.672 1.690 (Fail to reject)
 Traditional corporations 2.234 1.430 (Reject)
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Author:Pao, Hsiao-Tien; Lee, Tenpao; Tompkins, Daniel L.
Publication:Journal of International Business Research
Geographic Code:9TAIW
Date:Jul 1, 2005
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