i University of Amsterdam
Economics and Business
Bachelor Specialization Finance and Organization
Board size and Performance of Large Industrial Companies
Author: Xin Shen
Student number: 10686770 Thesis supervisor: Jan Lemmen Finish date: June 2017
ii Statement of Originality
This document is written by Student [Xin Shen] who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
iii Abstract
This paper estimates the effect of board size on performance of 41 industrial sector companies listed on the S&P during the period of 2010 to 2015. There are 246 observations in total. These large companies tend to have large board sizes, while not many studies have focused on the board size effect on large companies. The key coefficients show negative signsboth under the ordinary least squares method and the fixed effects method . However, a statistically significant strong negative impact only exists for Tobin’s Q under the fixed effects method in this paper. There are no
significant strong effects in other situations. However, since Tobin’s Q contains both backward and forward looking and it has a mix of accounting and market information, ROA only contains backward looking and has only historical information. So the results under Tobin’s Q are more trustworthy than those of ROA. Therefore, the null hypothesis should be rejected. The results are robust across econometric models that control for diversification, leverage ratio, firm size and investment opportunity. The results are consistent with most previous studies that firm performance improves as the board size shrinks.
Keywords: Corporate Governance, Board Size, Boards of Directors, Firm Performance
iv CONTENTS
Abstract iii
Section I Introduction 1
Section II. Literature Review 2
Section III Data Description and Model Setup 7
Section IV Results 9
Section V Discussion and Conclusion 12
1 I. Introduction
The relationship between characteristics of a company board and firm performance is still a fundamental issue in corporate governance. Researchers want to find an optimal board structure, for it is closely correlated with a board’s ability to make decisions. This, thus, will affect the firm performance. Board size is one of the crucial factors that influences a board’s abilities. Therefore, understanding the relationship between board size and firm performance is important. Early consensus was that an enlarged board was detrimental to a firm’s effectiveness. For example, Jensen (1993) found there was an inverse relationship between board size and firm performance. He even suggested that limiting the board size had a high priority in modern industrial revolution. Some recent studies seem to hold opposite opinions. Some of them argue that the costs of large boards had been overlooked. One recent study even showed that there was a positive link between board size and firm value, especially for firms with greater advisement requirements and higher degrees of diversification and financial leverage (Coles, Daniel and Naveen, 2008). However, the authors pointed out that these findings had not been definitively resolved by academic research and highlighted the need for further research on the relationship between board size and firm performance. So this paper will extend the prior research by adding firm performance into consideration. Most previous studies tended to concentrate on small to medium sized firms (e.g., Eisenberg, Sundgren and Wells, 1998; and e.g., Yermack, 1996). Little attention has been paid to large companies. However, it is quite common for a large company to have a bigger board. And more and more of these companies are forced to shirk their board sizes to please or attract investors (Baysinger and Butler, 1985), for some institutional investors, like California Public Employees’ Retirement System (CalPERS),
announced their tendencies of investing into companies with smaller sized boards (Wu, 2004). Thus, the research question of this paper is to estimate whether the inverse relationship between board size and firm performance in large industrial sector
companies exists. To examine this question, both the ordinary least squares method and the fixed effects method will be used. Tobin’s Q and Return on Assets (ROA) both are
2 employed to represent a company’s performance. 41 industrial sector corporations in S&P 500 from 2010 to 2015 are observed. Besides board size, variables like
diversification, financial leverage, firm size and investment opportunity are introduced as control variables. Based on the previous research papers, an inverse effect is
supposed to exist. Because when a board size increases, problems and costs may outweigh the benefits.
The reminder of this paper is organized in the following way. The next section is a review of prior literature about board size and firm performance. Then the hypothesis based on the literature will be introduced. The third section is a description of how the data is collected and what kind of model to be estimated. Then, the main results will be presented and discussed in section IV. The final section concludes this paper and discuss limitations in this paper. Then, some further research suggestions are given. II. Related literature and hypothesis
Company boards consist of executive directors and non-executive directors. Executive directors formulate strategy and perform routine tasks, while non-executive directors monitor the top management and advise their CEOs (Lipton and Lorsch, 1992). For large companies, more board members are needed to share heavier workloads and provide advice to CEOs. Therefore, it is not surprising for larger firms to obtain larger board sizes. However, whether the influence of an enlarged board on firm performance is positive or negative is still a controversy. General management literature typically recognizes that both advantages and disadvantages exist when adding new directors into boards (e.g., Hackman 1990).
Some recent studies did find that a large board size did not necessarily hurt firm performance, especially for large companies (e.g. Larmou and Vafeas, 2010). For those who believe there is a positive relationship between board size and firm performance, their main argument is that small sized boards cannot fit in large complex companies. As one prior research found, the greater the need for effective external linkage, the larger the board should be (Pfeffer and Salancik, 1978). For example, larger firms are
3 likely to have more external contracting relationships. Therefore, more board members are required to provide advice in obtaining contracts (Booth and Deli, 1996). Besides, with a larger board size, the top managers can gather greater collective information and more creative ideas (Coles et al., 2008). Another reason is about monitoring. Some scholars found that larger boards offer better monitoring quality. And this effect is more obvious when more non-executive directors are added into the boards. Non-executive directors might have greater incentives to monitor the top managers than executive directors, for they need to signal their managerial ability to the externals (Fama and Jensen, 1983). If the non-executive directors do not perform the monitoring job effectively, not only their reputations will suffer, but the probability of further
employment may decrease as well. Under the high supervisory quality, top managers are stimulated to focus on the interests of companies and shareholders (Monks and Minow, 1995). Besides, a large board size can mitigate the possibility of CEOs to maintain their power and seek profits at the expense of the companies (Dalton, Daily, Ellstrand and Johnson, 1999). The reason behind is that a large board is more likely to form an alternative political coalition to challenge their CEOs and take control over the firm than a small board. TIAA-CREF, one of the largest pension fund company in the world, stated that it would only invest in companies that have large amount of
non-executive directors on the boards (Coles et al., 2008). Their paper pointed out that the complexity of a company could offset the negative effects of board size on firm performance. Some recent studies did find that there was a positive impact of board size on firm value in large companies. And some researchers even claimed that a large board size may be an optimal value maximizing outcome for such firms (Coles et al., 2008). The supporters also claim that the recent opposite results imply that there may exist some potential advantages of enlarged boards which have been neglected or
underestimated by earlier studies (Larmou and Vafeas, 2010).
However, some researchers hold opposite opinions. The main arguments of them are that inefficiency problems and agency problems will arise under a large board size (Eisenberg et al, 1998). Firstly, it is more unlikely for directors in a large board to share
4 a common opinion. To implement policies effectively, members have to spend time in communicating with each other and reaching an agreement based on different opinions (Lipton and Lorsch 1992). Thus, there would be a tradeoff between efficiency and effectiveness. On the other hand, agency problems are less likely to happen in a small board (Eisenberg et al., 1998). Small and medium sized firms are more closely held or board members in these kinds of companies tend to have greater levels of stocks (Yermack, 1996). So their compensations were more correlated with firm performance. However, for large boards, the stocks board members owned are limited. Researchers found that the directors in America only owned 1% stocks in total (Bhagat and Black, 1996). Since the incentive is quite low, problems like free riding and social loafing are more likely to happen under a large board size (Kidwell & Bennett, 1997). In addition, some studies found that the reason why large companies obtained large board sizes are caused by several transaction costs (Coles et al., 2008). For example, “firing a director purely for downsizing reasons could damage the firm’s reputation for honoring implicit contracts and therefore its ability to recruit in the future.” (Coles et al., 2008, p. 335) This may be a reason why large companies fail to move to an optimal board size within a short time period. Studies have shown that large firms do have smaller board size targets, although they tend to take longer time to reach these targets (e.g., Wintoki and Yang, 2007). Some studies also pointed out that a large board was unnecessary, for other governance mechanisms, like corporation law, the internal structure of the firm, the capital market and so on, can substitute for a strongly independent board
(Williamson, 1983).
The other arguments tend to focus on non-executive directors. It seems that biases against risk-taking rise when the number of non-executive members increase (Jensen, 1993). The shares that non-executive directors hold usually are negligible. Therefore, they bear a reputation cost when a project fails, while their gain is limited when it succeeds (Eisenberg et al., 1998). This asymmetry implies that the non-executive directors tend to prefer safer but lower-return projects than risky but higher-return projects. In addition, some prior studies pointed out that the monitoring quality did not
5 improve with the number of board members growing (Jensen, 1993) . Actually, non-executive directors normally do not criticize the policies of top managers (Jensen, 1993). Besides, they seldom interfere with their CEOs’ decisions (Lipton and Lorsch, 1992). Or their interferences are too late, often after the shareholders have already lost value or the company has lost its competitive market position. These problems are more visible under larger boards. Even though directors bear the reputation costs, the
responsibilities of poor decision making are spread among more people, thereby cushioning the effects of an individual decision maker (Jensen, 1993).
Some of the researches are silent on the net effect of board size and firm
performance, for their results suggested there was no relationship between board size and firm value and some limitations may exist that affect the reliability of previous researches (e.g., Hermalin and Weisbach, 1988; and e.g., Gilson, 1990). A clear problem in studying board size effect is that the number of directors might be an endogenous variable, determined by other variables, such as company size, firm performance, or their CEOs preferences and characteristics. Some studies found that an aggressive CEO was more willing to be surrounded by a small board size, while some CEOs wanted to dress up the firm boards with more non-executive directors to please the shareholders and other investors (Byrd and Hickman, 1992). Although they indeed found significant results to conclude that the board size might contribute to firm value in their paper. But they highlighted that the possibility that the complex associations between board size and other variables might affect the results (Byrd and Hickman, 1992).
In summary, the general literature has agreed that there is an apparent tradeoff when adding more members into boards. But, collectively, the majority of studies seems to imply that smaller boards are optimal. The costs seem to outweigh its benefits under a large board size. And some institutional investors tend to prefer investing into large companies with smaller sized board. All these have forced companies to
implement the “smaller board” policy and shrink their board sizes. To see whether this policy works for large companies, the relation between board size and large industrial
6 sector companies will be estimated. The null hypothesis of the research question is that board size has no relationship with firm performance. While the alternative hypothesis is that board size has an inverse relationship with firm performance. The following section will estimate whether the null hypothesis could be rejected or not. Table 1 summaries some prior literature. Their methods and results are also pesented. Table 1: Literature review about board size and firm performance
Author(s) (Publication year) Region Time period Method(s) Performance measure
Board size Control variables Yermack (1996) US 1984 to 1991 OLS + Fixed effects model Tobin’s Q ROA, Investment opportunities. Diversification ，Firm size - 0.428 - 0.337 Eisenberg, Sundgren, and Wells (1998) Finnish 1992 to 1994
OLS ROA Firm size,
Board quality, Firm age Change in assets, Investment opportunities -1.410 Cheng (2008) US 1998 to 2007
OLS Tobin’s Q Firm size, Debt, Age, Diversification -0.342 Coles, Daniel and Naveen (2008) US 1992 to 2001
OLS Tobin’s Q Firm size, Advice, Leverage, Diversification +0.185 Guest (2009) UK 1981 to 2002 OLS + Fixed effects + GMM
Tobin’s Q ROA, Firm age, Debt, Investment opportunities, Firm size −0.023 −0.011 −0.019 Larmou and Vafeas (2010) US 1978 to 2009 OLS Tobin’s Q ROA Debt, Firm Age +0.023 +0.009
7 III. Data description and model setup
I am interested in investigating the relationship between firm performance and board size for large American industrial companies. Previous researchers mainly concentrate on data from small to medium sized companies from the American continent. So I want to explore whether this effect exists in large companies as well. Therefore, companies listed in the S&P 500 industrial sector based on their market capitalizations are chosen. I use a sample selection rule that requires each company to exist on the list during the period between 2010 and 2015. There are 68 companies on the list. However, only 41 companies’ data can be found during that period. So there are 246 observations in total. All the data discussed below is collected from Wharton Research Data Service
(WRDS). The dependent variable is firm performance. My econometric approach closely follows that of Cheng (2008) and Larmou and Vafeas (1996), using two methods to measure company performance. One is return on assets ratio (ROA). It is a company’s net income divided by its total assets. This ratio captures the effects on profitability and examines whether assets have been allocated efficiently. The other is Tobin’s Q. It is a measurement for stock market performance. Tobin’s Q is defined as the market value of a company divided by the replacement value of the firm's assets. Since it is difficult to find data on replacement value of firm assets, in this paper, Tobin’s Q is calculated by the market value of a company divided by the book value of total assets. This approximation is consistent with much of the literature. I use these two alternative measurements as a diagnostic check.
The key explanatory variable is board size. It is measured by the logarithm of the number of board directors. The logarithm is introduced as in the previous studies, for the literature has shown that the relationship between board size and performance is convex rather than linear (e.g, Yermack 1996). In the data, the board sizes of these companies range from 6 to 17. And the mean of the board size is about 10.569. In addition to board size, my regression includes diversification, leverage ratio, firm size and investment opportunity as control variables. Diversification is likely to affect a company’s board size, for its board may need to increase when making acquisitions or
8 seeking outside expertise (Coles et al., 2008). Diversification is measured by how many business segments these companies have. Leverage ratio also has an effect on firm’s performance (Coles et al., 2008). It is calculated by total debts divided by total firm assets. Besides, large firms are likely to have more external contracting relationships and may obtain larger boards (Pfeffer and Salancik, 1978). Firm size is also introduced as a control variable. It is represented by the logarithm of total assets. Many theories have argued that a firm’s value also depends on its investment opportunities (e.g., Smith and Watts, 1992). To measure investment opportunities, the total capital
expenditure divided by total firm assets is used as in other studies. All the data collected is fiscal yearly data. And they are based on the end of each year. The unit of the financial figures is dollars in million. To ensure the variables are not highly correlated, multicollinearity is tested as well. Table 1 presents a summary of characteristics of the data collected. The number of observations, mean values for key variables, standard deviation, maximum values and minimum values are presented. The final column is about correlations of control variables with the log of the board size. Finally, there is an error term that is assumed to be a normal, identical and independently distributed random variable. Moreover, it is assumed that the error term is not serially correlated. Both results are based on the fixed effects and the ordinary least squares method to mitigate the existence of unobservable characteristics that may affect firm
performance. Although the ordinary least squares method is widely used in prior studies, some studies have argued that the fixed effects estimation is a more unbiased method for testing panel data set (Hausman and Taylor, 1981).
Model:
Tobin’s Q=α+β1*Log(board size)+β2*Diversification+β3*Leverage+β4* Firm Size+β5*Investment Opportunity +Error
ROA =α+β1*Log(board size)+β2*Diversification+β3*Leverage+β4* Firm Size+β5*Investment Opportunity +Error
9 Table 2: Summary of the observations (in $ million)
Variables Observations Mean Standard deviation
Min Max Correlations to log (board size)
Board Size 246 10.569 1.919 6 17
Log (board size) 246 1.017 0.079 0.778 1.230
Diversification 246 5.512 3.972 1 19 0.368 Leverage 246 0.249 0.166 0 0.832 0.244 Firm Size 246 4.095 0.551 3.105 5.876 0.687 Investment Opportunity 246 0.045 0.049 0.003 0.256 0.145 Assets 246 35,221.017 101,653.976 751,216 1,273.984 Tobin’s Q 246 1.007 0.684 0.131 3.869 ROA 246 0.082 0.061 -0.128 0.389
IV. Results and discussion
The econometric results are presented in Table 2, using Tobin’s Q and ROA as the dependent variable and the log of the board size as the key explanatory variable. The second and third columns report the Tobin’s Q and ROA based on the pooled ordinary least squares method, while the last two columns report the Tobin’s Q and ROA based on the fixed effects method. The regressions of the two methods are exactly the same expect that one control variable “Diversification” is deleted under the fixed effects methods. Because it is assumed that the business segments of these companies are constant during 2010 to 2015 for simplification. However, only time varying variables need to be tested under the fixed effects model. As most previous papers, the R squares and the adjusted R squares under the fixed effects methods are much smaller than those of the OLS methods (e.g., Yermack, 1996; and e.g., Guest, 2009).
10 Table 3: Impacts of board size on firm performance
OLS Fixed Effects Variables Tobin’s Q ROA Tobin’s Q ROA Log (board size) -0.242
(0.563) 0.090 (0.070) -1.418** (0.673) 0.030 (0.094) Diversification 0.001 (0.011) 0.002** (0.001) (Deleted) (Deleted) Leverage 1.407*** (0.234) -0.125*** (0.029) 1.094** (0.660) -0.061* (0.036) Firm Size 0.634*** (0.098) -0.050*** (0.010) 0.011 (0.432) -0.001 (0.035) Investment Opportunity 3.166*** (1.127) -0.117** (0.058) 0.427*** (0.146) -1.890 (2.310) _cons -1.841*** (0.412) 0.220*** (0.041) 2.216* (1.599) 0.052 (0.093) Observations 246 246 246 246 R square 0.468 0.263 0.059 0.037 Adjusted R square 0.457 0.248 0.043 0.021
Note: Robust standard errors are indicated between parentheses. Significant at 1% (***), 5% (**), and 10% (*) levels
Only the coefficient under the fixed effects model shows a significant inverse result. And it is in the 5% significance level. The inverse relationship is consistent with most of the previous studies (e.g., Jensen, 1993; and e.g., Eisenberg et al., 1998). The coefficient is -1.418. This implies if the board size doubles, the firm’s Tobin’s Q will fall by about 42.686% (= -1.418 * log2). If expanding a ten people board by one member, there will be a reduction in Tobin’s Q by about 5.869% (= -1.418 * (log 11 – log 10)). Adding a director to a 15 people board will reduce Tobin’s Q by about 4.974% (= -1.418 * (log 16 – log 15)). This change is economically remarkable. For this sample, the average of Tobin’s Q is around 1, while the mean of these firms assets is $35,221.017 million (see table 1). It there is 1 percentage change in Tobin’s Q, the value of firm will have a change of $352 million approximately. However, although there is also an inverse relationship between board size and Tobin’s Q under the OLS
11 method, it is not significant at 10% level (P value =0.123). The coefficients of ROA do not show any significant inverse relationships under both methods. However, Since Tobin’s Q contains both backward and forward looking and it has a mix of accounting and market information, ROA only contains backward looking and has only historical information. The results under Tobin’s Q are more trustworthy than those of ROA. So the null hypothesis should be rejected. The results are consistent with the majority of the previous literature (e.g., Eisenberg, 1998; and e.g., Yermack, 1996)
In order to correct for clustering of standard errors within companies, standard deviations are robust. The coefficients of leverage, firm size and investment
opportunity show strong significant results under the OLS method (at 1% significance level). Only the coefficients of leverage and investment opportunity show significant results under the fixed effects method.
Although the coefficient of diversification using ROA measurement is significantly positive (at 10% level), the coefficient is still quite small and close to 0. This implies that diversification does not have a strong impact on a firm performance. This is consistent with the Berger and Ofek’s research (1995). The coefficients of leverage ratio have negative signs with ROA as expected. According to Myers (1984), firms preferred internal financing than issuing debts. ROA reflects a company’s ability to make profits. If they have high net incomes, they do not want to issue debts, for they have several restrictions to use the debts. For example, they are forced to use the debts to some specified projects. Leverage ratios are positively related with Tobin’s Q. These results are supported by Lang, Ofek, and Stulz’s finding (1996) that leverage ratio did not hurt Tobin’s Q of large companies. The results of firm size are similar to those of the leverage ratio. The coefficients are negative under the ROA measurement, while they are positive under the Tobin’s Q measurement. As mentioned above, large companies are more inert (Eisenberg et al., 1998). They prefer safer projects than higher-return projects. This explains why firm sizes and ROA ratios are negatively correlated. Scholars, like Stanwick and Stanwick (1998), found a significantly positive relationship between firm sizes and firm financial performances, which are
12 measured by Tobin’s Q. Large firms are more likely to perform well . According to Myers (1977), investment opportunities were positively correlated with a company’s future performance, while Tobin’s Q measurement is widely used to represent a company’s future performance. The positive sign of investment opportunity is consistent with Szewczyk, Tsetsekos and Zantout’s finding (1996) that investment opportunities were positively correlated with firms’ Tobin’s Q. The negative
relationship between investment opportunities and ROA ratios can be caused by the characteristics of industrial companies(Sun, Lan and Ma, 2014). They found that most of the investment opportunities in industrial companies are projects, instead of
investments. Projects need longer time periods to generate profits. That is why it is negatively correlated with ROA ratios.
V. Conclusion
Large industrial sector companies in US from 2010 to 2015 are tested in this paper. Although the statistically significant strong inverse relationship only exists for Tobin’s Q, other results are not significant. Tobin’s Q captures more firms’ historical and future performance and is more trustworthy. Therefore, the null hypothesis should be rejected. The results are both robust under the OLS method and fixed effects method after controlling for diversification, leverage ratio, firm size and investment opportunity. This finding is consistent with most other papers. Most of them found that the firm performance improved after decreasing the board size. One study even found that the negative board size effect was strongest in UK large companies (Guest, 2009). There are some limitations in the regression that may affect the results. Firstly, the error term is assumed to be independent and not serially correlated. However, in reality, the error term is unlikely to meet this assumption. Secondarily, business segments provided by WRDS only include large categories. Some small sectors are combined together as “other” category. This may be a reason why diversification does not has a positive effect on firm performance as expected. Besides, only a small number of companies are included. The small sample size may cause coverage bias and, thus,
13 affect the results. Last but not least, it is assumed in this paper that firm performance depends on the quality of monitoring and decision-making by the board directors. Therefore, the board size is an important determinant of its performance. However, in reality, some scholars (e.g. Gilson, 1990) pointed out that firm value, firm size and board size may be interacting with each other. It can be seen in table 1 that the
correlation coefficient between the log of board size and firm size is much higher than others. Even though it is still below 0.7, the high correlation may affect the results as well.
Further researches about whether the “one fit all” policy fits for large companies should still be examined. The board sizes had few changes during 2010 to 2015. This could be caused by the high transaction costs to adjust the board size suddenly. A large company size and a longer time period, which contains more changes in board sizes are suggested to be used in future researches.
14 References
Baysinger, B. D., & Butler, H. N. (1985). Corporate governance and the board of directors: Performance effects of changes in board composition. Journal of Law,
Economics, & Organization, 1(1), 101-124.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of
financial economics, 37(1), 39-65.
Bhagat, S., & Black, B. S. (1996). Do independent directors matter? Center for Law
and Economic Studies, Columbia University School of Law
Booth, J. R., & Deli, D. N. (1996). Factors affecting the number of outside directorships held by CEOs. Journal of Financial Economics, 40(1), 81-104.
Byrd, J. W., & Hickman, K. A. (1992). Do outside directors monitor managers?: Evidence from tender offer bids. Journal of Financial Economics, 32(2), 195-221. Coles, J. L., Daniel, N. D., & Naveen, L. (2008). Boards: Does one size fit all?. Journal
of Financial Economics, 87(2), 329-356.
Dalton, D., Daily, C. M., Johnson, J.L., & Ellstrand, A.E. (1999). Number of directors and financial performance: A meta-analysis. Academy of Management Journal 42(6), 674.
Eisenberg, T., Sundgren, S., & Wells, M. T. (1998). Larger board size and decreasing firm value in small firms. Journal of Financial Economics, 48(1), 35-54.
Fama, E. F., & Jensen, M. C. (1983). Agency problems and residual claims. The
Journal of Law and Economics, 26(2), 327-349.
Gilson, S. C. (1990). Bankruptcy, boards, banks, and block holders: Evidence on changes in corporate ownership and control when firms default. Journal of Financial
15 Guest, P. M. (2009). The impact of board size on firm performance: evidence from the UK. The European Journal of Finance, 15(4), 385-404.
Hackman, R. (1990). Groups that work (and those that don’t). San Francisco: Jossey-Bass.
Hausman, J. A., & Taylor, W. E. (1981). Panel data and unobservable individual effects. Econometrica: Journal of the Econometric Society, 49(6), 1377-1398
Jensen, M. C. (1993). The modern industrial revolution, exit, and the failure of internal control systems. Journal of Finance, 48(3), 831-880.
Kidwell, R. E., Mossholder, K. W., & Bennett, N. (1997). Cohesiveness and organizational citizenship behavior: A multilevel analysis using work groups and individuals. Journal of Management, 23(6), 775-793.
Lang, L., Ofek, E., & Stulz, R. (1996). Leverage, investment, and firm growth. Journal
of Financial Economics, 40(1), 3-29.
Larmou, S., & Vafeas, N. (2010). The relation between board size and firm performance in firms with a history of poor operating performance. Journal of
Management and Governance, 14(1), 61.
Lipton, M., & J.W. Lorsch. (1992). A modest proposal for improved corporate governance. Business Lawyer, 48(1), 59–77.
Monks, R. A. G., & Minow, N. (1995). Corporate governance. Basil Blackwell. Cambridge.
Myers, S. C. (1977). Determinants of corporate borrowing. Journal of financial
economics, 5(2), 147-175.
Myers, S. C. (1984). The capital structure puzzle. The Journal of Finance, 39(3), 574-592.
16 Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource
dependence approach. New York. Harper.
Smith, C. W., & Watts, R. L. (1992). The investment opportunity set and corporate financing, dividend, and compensation policies. Journal of Financial Economics,
32(3), 263-292.
Stanwick, P. A. & S. D. Stanwick. (1998). The Determinants of Corporate Social Performance: An Empirical Examination. American Business Review, 16(1), 86–93. Sun, J., Lan, G., & Ma, Z. (2014). Investment opportunity set, board independence, and firm performance: The impact of the Sarbanes-Oxley Act. Managerial Finance, 40(5), 454-468.
Szewczyk, S. H., Tsetsekos, G. P., & Zantout, Z. (1996). The valuation of corporate R&D expenditures: Evidence from investment opportunities and free cash flow.
Financial Management, 25(1), 105-110.
Williamson, O. E. (1983). Organization form, residual claimants, and corporate control. The Journal of Law and Economics, 26(2), 351-366.
Wintoki, M. B., & Yang, T. (2007). Do firms adjust to a target board structure. University of Georgia
Wu, Y. (2004). The impact of public opinion on board structure changes, director career progression, and CEO turnover: Evidence from CalPERS’ corporate governance program. Journal of Corporate Finance, 10(1), 199-227.
Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics,40(2), 185-211.
