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Re-measuring agency costs: The effectiveness of blockholders
The Quarterly Review of Economics and Finance 47 (2007) 588–601
Re-measuring agency costs: The effectiveness of blockholders Xiaoying (Cindy) Chen ∗ , Jasmine Yur-Austin Department of Finance, California State University, Long Beach, United States Received 1 April 2007; accepted 6 September 2007 Available online 5 October 2007
Abstract This study examines the effectiveness of blockholders in mitigating agency costs such as managerial extravagance, poor asset management and underinvestment. Our evidence suggests that outside and inside blockholders exert their interventions differently. We find that outside blockholders are more vigilant about mitigating managerial extravagance while inside blockholders, especially managerial blockholders, are more effective in improving the efficiency of firm asset utilization. However, only managerial blockholders significantly overcome underinvestment problems, which may be attributable to their duality roles. Published by Elsevier B.V. on behalf of the Board of Trustees of the University of Illinois. JEL classification: G30; G32 Keywords: Blockholders; Agency costs of equity; Managerial extravagance; Underinvestment; Contracting market
1. Introduction Jensen and Meckling (1976) define an agency problem as a situation in which managers (“agents”) take actions that contravene shareholders’ (“principals”) interests. Furthermore, mounting evidence in recent years suggests that managerial agency problems hamper the growth and the value of firms. A record number of top corporate executives face serious incarceration for their alleged financial crimes, such as presiding over massive corporate fraud and conducting illegal insider trading.1 Consequently, re-measuring agency costs and the effectiveness of corporate governance mechanisms deserves more academic study. ∗
Corresponding author. Tel.: +1 562 985 5072; fax: +1 562 985 1754. E-mail address: [email protected] (X. Chen). 1 Some famous examples are Ex-CEO of Tyco International executives Dennis Kozlowski and Mark Swartz, who were sentenced from 8 to 25 years in prison for raiding Tyco of hundreds of millions of dollars to pay for their extravagant 1062-9769/$ – see front matter . Published by Elsevier B.V. on behalf of the Board of Trustees of the University of Illinois.
doi:10.1016/j.qref.2007.09.003
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Traditionally, financial economists have suggested that ownership structure tends to mitigate the conflicts of interest between shareholders and managers, as well as other governance mechanisms such as executive compensation structure, board directors’ composition, and the threat of a hostile takeover (e.g., Shleifer & Vishny, 1997; Yermack, 1996). In this study, we are particularly interested in re-examining the effectiveness of blockholders in mitigating agency costs.2 Considerable prior empirical evidence suggests that concentrated shareholders (i.e., large blockholders) seem to influence managerial decisions. For example, Brickley, Lease, & Smith (1994) and Demsetz and Lehn (1985) suggest that large shareholders are likely to reduce the magnitude of managerial expropriation. Large shareholders may control the corporate board and select the company’s top managers. Hence, the critical role of blockholders in corporate governance should not be underestimated. Our analysis of blockholders in this study is based on dataset reported by Dlugosz, Fahlenbrach, Gompers, & Metrick (2006) (hereforth DFGM). Companies disclose their blockholders in their proxy statements. Due to the difficulty of collecting data directly from proxy statements, most prior studies use Compact Disclosure Database to identify large shareholders along with their shareholdings (e.g., Anderson & Lee, 1997). Nevertheless, DFGM disclose that Compact Disclosure Database fails to record footnotes of some corporate annual proxy statements, which leads to double counting the same ownership or mislabeling preferred stockholdings as common stockholdings. DFGM argue that the errors in Compact Disclosure may be responsible for some insignificant empirical results reported in earlier studies pertaining to large shareholders.3 Accordingly, DFGM compare the Compact Disclosure Database with the original proxy statements and produce a valid blockholders dataset. Our study adopts DFGM’s dataset (2006), which is likely to produce less biased test results and thus improve the validity of our findings. This study examines three types of agency costs: managerial extravagance, poor asset management, and underinvestment, which may arise when managers are incompetent or pursue their own interests at the expense of shareholders’ well-being. The study addresses the following questions. First, can blockholders act as effective monitoring mechanisms in reducing the three types of previously mentioned agency costs? Second, do outside blockholders or inside blockholders act more effectively? That is, which group of blockholders is most effective monitoring each of the agency costs? Third, do managerial blockholders (blockholders who are managers themselves) serve as better monitoring mechanisms? Finally, does the monitoring effectiveness of blockholders change from an expanding market to a contracting market? Our study focuses on large publicly traded companies over a 5-year period (from 1996 to 2001). The most relevant paper to ours is Singh and Davidson (2003), which focuses on a randomly selected sample of 118 large firms for year 1992 and year 1994. With a larger sample over a longer time period, we find that overall, large shareholders can constrain the above agency problems. Different from the findings of Singh and Davidson (2003), we find that both outside and inside blockholders significantly reduce managerial extravagance and facilitate asset utilization. personal spending. Former WorldCom Chairman Bernard J. Ebbers was sentenced to 25 years in prison for scheming in an $11 billion accounting fraud. In addition, John J. Rigas, the founder of Adelphia Communications Corp., received 15 years in prison for stealing millions from the cable company for personal business loans, moreover hiding more than $2.3 billion in debt, and lying to shareholders. 2 Rules 13d-1 through 13d-7 of the Securities Exchange Act of 1934 provide legal definitions of blockholders as those who own at least 5% of a firm’s outstanding common shares. 3 DFGM suggests that incorrect blockholder data is problematic when the data are used as independent variables but are less problematic when used as dependent variables. Our study uses blockholder data as independent variables; therefore, the adoption of DFGM’s sample is essential to justify our findings.
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Particularly, outside blockholders are more efficient in reducing managerial discretionary expenses while inside blockholders, especially managerial blockholders, are more efficient in enhancing asset utilization. Finally, our findings indicate that managerial blockholders significantly attenuate the underinvestment problems. These results are robust from an expanding market (1996–1999) to a contracting market (2000–2001).4 The study is structured as follows. Section 2 defines the proxies of agency costs and states the hypotheses. Section 3 describes sample firms along with our empirical approach. Section 4 presents empirical evidence as well as the interpretations. Section 5 offers conclusions. 2. Hypotheses related to proxies of agency costs In this section, we discuss the expected association between block ownership and three practical measures of agency costs: SG&A ratio (for managerial extravagance), asset turnover ratio (for asset management quality) and adjusted short-term debt ratio (for likelihood of involving in underinvestment). 2.1. Block ownership and managerial extravagance Managerial extravagance represents excessive managerial discretionary spending that is detrimental to corporate earnings. Managers may seek personal benefits at the expense of shareholders’ expenses, e.g., excessive compensation packages and lavish perquisites such as posh offices and country club memberships. Blockholders, having larger stakes invested in the firm, have more incentives to monitor these managerial opportunistic behaviors than smaller shareholders. When managerial extravagance arises, blockholders can cast the deciding votes to exert extra pressure on managers to control their discretionary spending. This incentive is particular strong for outside blockholders, who recently have become more actively involved in corporate governance (e.g., Smith, 1996). In contrast, since managerial blockholders enjoy, to some extent, private and personal benefits of corporate control (e.g., Holderness, 2003) that are not shared with other shareholders, managerial blockholders may not have the same incentives as outside blockholders to curtail managerial spending. Singh and Davidson (2003) use the SG&A ratio (Selling, General, and Administrative expenses/Total Sales) to capture how vigilant managers are in controlling their discretionary spending.5 We expect that block ownership is negatively related to this ratio, with the association being more significant for outside block ownership. 2.2. Block ownership and poor asset management Shareholders hire managers as “agents” to preside over business operations and implement corporate strategies, expecting that managers can effectively deploy the firms’ assets in pursuing optimal investment results. However, managers may fail to maximize shareholders’ benefits because of managerial incompetence. As a result, blockholders have good reason and great incentive to monitor the managers’ business decisions. Hence, we hypothesize that a firm with large block ownership will make better decisions regarding the firm’s assets. This monitoring should 4 DJIA (Dow Jones Industrial Average) gained 124.68% within 1996–1999 but lost approximately 12.83% from 2000 to 2001. 5 SG&A represents expenses incurred in regular business operation such as accounting expenses, advertising expenses, and labor related expenses (including salary, pension, retirement, and other employee benefits).
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be more effective when ownership and management are combined (e.g., Demsetz & Lehn, 1985). Therefore, we also hypothesize that managerial blockholders will deploy corporate assets more effectively than outside blockholders do. In earlier studies (Ang, Cole, & Lin, 2000; Singh & Davidson, 2003), the asset turnover ratio (Total Sales/Total Assets) has been used to evaluate asset utilization efficiency. We expect the asset turnover ratio is positively related to block ownership; this is especially significant for managerial block ownership. 2.3. Block ownership and underinvestment Underinvestment refers to the situation where a firm forgoes positive net present value projects. While shareholders would like managers to exploit a firm’s potential as much as possible by undertaking riskier projects, managers who have most of their human capital at risk with their current employers may prefer to invest in safer projects. Thus, blockholders would like to exert their influence by restraining any potential underinvestment possibilities. Bryan, Nash, & Patel (2006) propose using the adjusted short-term debt ratio (Market value of equity/Book value of equity multiplied by the Short-term debt ratio) to measure the likelihood that a firm is engaged in underinvestment. Because short-term debt needs to be repaid (or rolled over) in a timely fashion, firms are prone to make any positive net present value investment decisions more quickly in an attempt to meet their financial obligations.6 Hence, a large portion of short-term debt in a capital structure is projected to reduce underinvestment potential, especially for a firm associated with high growth opportunities (high Market value of equity/Book value of equity). Extending their argument, we expect that the adjusted short-term debt ratio is positively related to block ownership. This association is likely to be more significant for managerial block ownership. 3. Sample firms description and empirical approach 3.1. Sample firms DFGM concentrate on large shareholder ownership of companies in IRRC (Investors Responsibility Research Center) from 1996 to 2001.7 Based on their definitions, we classify blockholders into two types: inside blockholders and outside blockholders. Inside blockholders consist of managerial blockholders, non-officer director blockholders, affiliated blockholders, and ESOP (Employees Share Ownership Plans)-related blockholders.8 The outside blockholders are those that are not defined in the above categories. Given that the characteristics of blockholders are different, we conjecture that the influence of each type of blockholders may vary concerning the reduction of managerial agency costs. Next, we match DFGM ownership data with COMPUSTAT Industrial Annual files. The latter will be used to calculate the agency costs ratios. There is a possible causality issue between own6 A short-term maturity strategy helps mitigate underinvestment even when the short-term debt is rolled over. The short-term debt is replaced with new debt, which will be repriced based upon the firm’s current investment performance. Thus, the intent of taking a positive net present value project will ease the concerns of the lenders on the solvency of new debt and further lower the interest costs. 7 DFGM (pp. 596–597, 2006) state that “the IRRC’s universe is drawn from the Standard & Poor’s (S&P) 500 as well as the annual lists of the largest corporations in the publications of Fortune, Forbes, and Business Week. It is about 1500 of the largest U.S. companies”. 8 An affiliated blockholder is the voting party, either a trust or any individual, who can be partially influenced by either officer or director.
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ership data and agency costs (e.g., Allen & Phillips, 2000; Bethel, Libeskind, & Opler, 1998). While blockholders might reduce agency costs, the inherent agency problems may also influence the investment decisions of large shareholders, and the ownership structure may change accordingly. Therefore, we carefully investigate when firms report their annual accounting statements and when firms file their proxy statements with the SEC. When we match block ownership from the DFGM dataset with accounting value from COMPUSTAT, we ensure that the proxy filing date is prior to accounting value disclosed date.9 By doing so, our dataset, to some extent, mitigates any potential causality issue and enables us to investigate the effect if block ownership leads to any reduced agency costs. 3.2. Descriptions of proxies of agency costs Table 1 reports the descriptive statistics for proxies of agency costs as well as block ownership. Statistics are presented both for the pooled sample and for each sample year. As shown in the table, while the means of agency cost measures (SG&A ratio, asset turnover ratio and adjusted short-term debt ratio) are stable from 1996 to 1999, they change significantly in 2000–2001. The pooled mean SG&A ratio for our sample is 0.25, which is slightly lower than Singh and Davidson (2003) who report an average SG&A ratio of 0.28. Our sample pooled mean asset turnover ratio (1.03) is also lower than the value (1.43) reported in Singh and Davidson (2003) and much lower than the value (4.76) presented in the study by Ang et al. (2000).10 Compared to Singh and Davidson (2003), our sample firms have a larger average size with sales of $4.8 billion and with total assets of $11.6 billion.11 This may explain why these two agency cost ratios have different values between our sample and that of Singh and Davidson (2003). The pooled mean of all block ownership is 23.40%, among which outside ownership is 16.27% and inside ownership is 7.13%. It indicates that all blockholders compromise about one-fourth of total shares outstanding and outside blockholders control the largest proportion of the blockholdings. Among different groups of inside blockholders, managerial blockholders hold 2.75%, while the non-manager blockholders (affiliated blockholders, non-officer director blockholders, and ESOP-related blockholders) count 4.39%. 3.3. Multivariate regression models Our main interest is the relation between the three types of agency costs (managerial extravagance, poor asset management and underinvestment) and block ownership. Many prior studies (e.g., Crutchley & Hansen, 1989; Jensen, 1986) have reported that firm size and leverage ratio have bearings on the magnitude of agency costs. Therefore, we incorporate these two factors into our multivariate analysis. Large firms usually draw more scrutiny from the capital market, which 9 The SEC requires that all beneficial owners of more than 5% of a company’s common stock should be listed in the company’s proxy statement (DEF14A). The filing date with the SEC may occur in any month of a year. In COMPUSTAT, fiscal years of accounting data ending January 1 through May 31 are treated as ending in the prior calendar year. Take the example of Oracle Corporation, whose fiscal year ends in May. Hence, its accounting value disclosed in May 1996 will be treated as annual data in fiscal year 1995. Because Oracle Corporation files its proxy with SEC on September 11, 1996, we should match this proxy (in September 1996) with accounting value disclosed in May 1997 (which is fiscal year 1996). 10 Unlike our study, Ang et al. (2000) obtain their sample firms from the Federal Reserve Board’s National Survey of Small Business Finances (NSSBF). 11 Singh and Davidson (2003) report the sample firms with total sales of $2.1 billion and total asset of $2.0 billion.
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Table 1 Sample firm characteristic descriptive statistics Pooled
1996
1997
1998
1999
2000
2001
SG&A ratio
Mean S.T.D N
0.25 0.33 4430
0.2 0.14 584
0.23 0.19 565
0.24 0.20 805
0.23 0.16 781
0.27 0.30 842
0.29 0.62 853
Assets turnover ratio
Mean STD N
1.03 0.77 5567
1.08 0.780 745
1.04 0.76 727
1.05 0.76 1022
1.06 0.78 983
1.01 0.78 1045
0.97 0.74 1045
Adjusted short-term debt ratio
Mean
1.18
1.17
1.20
1.42
1.30
1.13
0.87
STD N
1.57 4675
1.22 608
1.50 600
2.12 861
1.85 842
1.40 879
0.87 885
Mean STD N
23.40 18.05 5572
20.67 17.16 746
20.87 17.17 727
23.76 18.43 1022
24.59 18.47 985
24.77 18.11 1046
24.27 18.11 1046
Mean
16.27
13.81
14.01
13.81
17.40
17.49
17.25
15.26 5572
13.89 746
14.03 727
15.43 1022
16.09 985
15.56 1046
15.38 1046
7.13
6.86
6.86
7.45
7.19
7.28
7.01
11.82 727
12.74 1022
12.31 985
11.93 1046
12.40 1046
Block ownership (%)
Outside block ownership (%)
STD N Inside block ownership (%)
Mean STD N
Non-manager inside block ownership (%)
Mean
12.26 5572
12.2612 746
4.39
4.56
4.57
4.36
4.20
4.34
4.39
9.74 5572
10.10 746
9.72 727
9.95 1022
9.32 985
9.19 1046
10.19 1046
2.75
2.30
2.29
3.09
3.00
2.94
2.62
STD N
7.76 5572
7.18 746
7.28 727
8.21 1022
8.24 985
7.95 1046
7.37 1046
Total assets ($ millions)
Mean STD N
11632.34 51323.34 5572
9383.21 32308.54 746
11178.75 44390.65 727
9454.43 39911.26 1022
111178.7594 12377.03 59419.50 57211.29 985 1046
13252.69 61325.17 1046
Total sales ($ millions)
Mean STD N
4805.78 11509.25 5569
4395.44 8966.18 745
4938.61 10230.07 727
4227.00 9866.69 1022
5130.02 11759.94 984
5045.71 13122.72 1046
5026.49 13339.45 1045
Total leverage
Mean STD N
0.59 0.24 5550
0.60 0.21 740
0.60 0.2 725
0.59 0.23 1017
0.59 0.23 981
0.59 0.29 1043
0.60 0.24 1044
STD N Managerial block ownership (%)
Mean
The sample used in this study consists of 1371 firms in Dlugosz, Fahlenbrach, Gompers and Metrick (2006) (hereforth DFGM) from 1996 to 2001. We match the block ownership data in DFGM to accounting data obtained from COMPUSTAT. SG&A ratio = Selling, General, and Administrative expense/Total sales; Asset turnover ratio = Total sales/Total assets; Adjusted shortterm debt ratio = [(Total assets − Book value of equity + Market value of equity)/Total assets]*(Short-term debt/Total debt); Block ownership = Percentage held by all blockholders for that firm-year; Outside block ownership = Percentage held by all outside blockholders; Non-manager inside block ownership = Percentage held by all affiliated blockholders, by all ESOP-related blockholders and by all non-officer director blockholders; Managerial block ownership = Percentage held by all managerial blockholders; Inside block ownership = Non-manager inside block ownership + Managerial block ownership; Total leverage = Total debt/Total assets.
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can be regarded as an external monitoring mechanism. Thus, it is often expected that the firm size and agency costs are negatively related. We measure the firm size by the natural log of a firm’s annual sales. As shown in Table 1, total sales are associated with extremely high standard deviations; it indicates a wide range of total sales values. We follow a common rescaling approach by taking logarithms of the original sales values. A caveat of this methodology is that it becomes difficult to quantify the relation between dependent variables and total sales. Since the purpose of this study is not to measure the magnitude in agency costs given changes in firm size, we use logarithmic values in our regression analysis. Creditors are also important stakeholders. As leverage increases, a firm’s default risk increases; thus, creditors have incentives to monitor managerial performance. We include total leverage (total debt ratio) as a control variable reflecting the extent of creditors’ incentives of serving as an external governance mechanism. Past research (e.g., Fama & Jensen, 1983; La Porta, Lopez-de-Silanes, & Shleifer, 1999) suggests that the effectiveness of monitoring or binding mechanisms arguably depends on exogenous variables, such as market conditions and industrial sectors. Accordingly, we add a contracting market dummy for years 2000 and 2001, and adopt interaction terms by multiplying explanatory variables with the contracting market dummy to indicate any variations in agency costs due to market conditions. Further, we define 48 industries according to COMPUTSTAT 4-digit SIC codes.12 Rather than using a simple pooled regression model, we employ a panel data model with fixed effects, taking into account magnitudes of agency costs attributable to any industry effects. This fixed effect model would have constant slopes but intercepts that differ according to industries.13 4. Empirical evidence and interpretations In this section, we present the results of our multivariate regression analysis relating agency cost proxies to block ownership, taking into account the controlled firm size, leverage, industry classification, and market conditions. Model 1 examines the relationship between agency costs and the total block ownership. Model 2 incorporates the contracting market effect to Model 1. Model 3 examines the impacts of outside block ownership and inside block ownership on agency costs. Model 4, modifying Model 3, encompasses the contracting market effect. Model 5 further examines how the outside block ownership, managerial block ownership, and non-manager block ownership relate to agency costs. Model 6, modifying Model 5, encompasses the contracting market effect. All of the six models are estimated by panel data regressions with fixed effects.14 4.1. Agency cost—managerial extravagance The dependent variable in Table 2 is the SG&A ratio, used as a proxy of managerial extravagance. Model 1 reports a significant negative relation (−0.0017, t = −5.94) between all block ownership and SG&A ratio. This relation is consistent when various combinations of contracting market dummies and the independent variables are added in Model 2. Model 3 reports similar findings, which show that both outside (−0.0019, t = −5.67) and inside block ownership (−0.0013, t = −3.33) are significantly negative related to SG&A expenditure. These relations are consistent 12 The industry definition can be found at Ken French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ data library.html. 13 We also ran a panel model with fixed effects for firms and obtained identical results. 14 Equations of all six models are presented in Appendix.
1
2
3
Constant Contracting market dummy
0.7185*** (25.85)
0.7549*** (21.52) −0.0941 −(1.64)
All block ownership Contracting market dummy
−0.0017*** −(5.94)
−0.0017*** −(4.72) 0.0000 −(0.01)
4
5
6
0.7200*** (25.89)
0.7567*** (21.54) −0.0947 −(1.64)
0.7176*** (25.69)
0.7537*** (21.34) −0.0931 −(1.64)
All outside block ownership Contracting market dummy
−0.0019*** −(5.67)
−0.0019*** −(4.46) 0.0000 (0.01)
−0.0019*** −(5.71)
−0.0020*** −(4.50) 0.0000 (0.03)
All inside block ownership Contracting market dummy
−0.0013*** −(3.33)
−0.0014*** −(2.72) 0.0000 (0.06)
All non-manager inside block ownership Contracting market dummy
−0.0016*** −(3.23)
−0.0016*** −(2.72) 0.0001 (0.14)
All managerial block ownership Contracting market dummy
−0.0009 −(1.41)
−0.0008−(1.00) −0.0002 −(0.18)
−0.0563*** −(16.47)
−0.0611*** −(14.10) 0.0125* (1.77) −0.0883** −(2.43) 0.0445 (0.75)
Control variables Firm size (sales) Contracting market dummy Financial Leverage ratio (total debt ratio) Contracting market dummy Adjusted R-squared Samplesize
−0.0565*** −(16.60) −0.0769*** −(2.70) 0.0637 4409
−0.0614*** −(14.23) 0.0126* (1.79) −0.0951*** −(2.64) 0.0462 (0.78) 0.064 4409
−0.0566*** −(16.63) −0.0734** −(2.56) 0.0643 4409
−0.0615*** −(14.25) 0.0126* (1.80) −0.0916** −(2.53) 0.0463 (0.78) 0.065 4409
−0.0707** −(2.45) 0.0651 4409
0.065 4409
The dependent variable is the proxy of managerial extravagance: SG&A ratio. * , ** , *** indicate statistically significant at the 10%, 5%, and 1% levels, respectively. The dependent variable is SG&A expense ratio, the proxy for managerial extravagance. Models 2, 4 and 6 is modifying Models 1, 3 and 5, adding market dummy interactive terms. Inside block ownership is defined as the sum of blocks held by (1) managerial blockholders; (2) non-manager director blockholders; (3) affiliated blockholders; and (4) ESOP (Employees Share Ownership Plans)-related blockholders. Block ownership held by shareholders not in these four categories is defined as outside block ownership. Block ownership held by managerial blockholders is defined as managerial block ownership. The contracting market dummy is an interactive term, equal to the product of independent variables times a dummy variable, “1” for year 2000 and 2001 and “0” for other years. A significant interactive term indicates that this independent variable’s’ effect on the dependent variable is different between 1996–1999 and 2000–2001. For example, the coefficient of the block ownership reflects the magnitude of changes in the dependent variable due to the changes in this block ownership from1996 to1999. For years 2000 to 2001, the magnitude of the impact is the sum of the coefficient of the block ownership plus the coefficient of the contracting market dummy, the interactive term. The control variable, firm size is defined as the log of annual total sales; leverage is defined as ratio of debt to total assets. To control for industry effects, we classify the sample firms into 48 industries based upon their four digit SIC code, then estimate the panel data models with fixed effect for these 48 industries.
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Table 2 Managerial extravagance and blockholders
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after contracting market effect variables are incorporated in Model 4. Accordingly, these results support the managerial extravagance hypothesis that blockholders’ monitoring influences managers to significantly curtail their discretionary spending. In contrast to Singh and Davidson (2003) who suggest outside blockholders have no significant effect on reducing managerial spending, our study finds that both outside and inside blockholders have significant influence of reducing managerial extravagance. Further, our evidence suggests that outside blockholders are more efficient in cutting the expenses than inside blockholders. We also investigate whether managerial blockholders exert their duality role to further control SG&A spending. Models 5 and 6 indicate that while outside blockholders and other inside blockholders significantly decrease such spending, managerial blockholders have an insignificant influence on it. This is consistent with Holderness’s (2003) argument that in some situations, managers’ private benefits of corporate control outweigh share benefits of control. Both of the external monitoring variables of firm size and leverage are negatively and significantly associated with the SG&A ratio in all six models, consistent with prior studies (e.g., Grossman & Hart, 1982; Singh & Davidson, 2003). Because larger firms draw more public attention and extensive analyst coverage and creditors restrain managers from accessing extra cash flow, discretionary spending is lower in larger firms and in firms with a high leverage ratio. 4.2. Agency cost—poor asset management In Table 3, the dependent variable is the asset turnover ratio which is a proxy for asset management quality. The six models present the same results that the coefficients of block ownerships are positive and significant. Taken together, the evidence implies that blockholders improve asset management quality, and inside blockholders, especially managerial blockholders, are more efficient. These findings are consistent with our asset management hypothesis and the results are robust after considering market conditions. Consistent with the Singh and Davidson (2003) study, the coefficients of firm size are significantly positive. It suggests that a large firm tends to capture more synergy gains throughout its business lines; hence, asset turnover efficiency tends to improve as a firm gets larger. The leverage ratio is significantly negatively related to asset turnover ratio. Noticeably, as shown in Model 6, the coefficient of leverage ratio becomes less negative from −0.6927 to −0.4028 (=−0.6927 + 0.2899) when the economy is contracting. It is possible that the creditors’ desire to improve asset management nevertheless outweighs a free-rider problem. When the economy is expanding, the prospects of the business are good; the creditors feel less need to supervise managers’ performance or to question their strategic decisions. Consequently, creditors tend to rely more on shareholders for monitoring management. However, when the economy is slowing down, the possibility of corporate financial distress increases and the firm’s default risk increases. Under this situation, creditors have more incentives to monitor a manager’s asset management effectiveness (e.g., Ang et al., 2000). 4.3. Agency cost—underinvestment The results concerning the relation between underinvestment and blockholders are presented in Table 4. According to Bryan et al. (2006), a firm with a high adjusted short-term debt ratio is less likely to forgo profitable projects. The dependent variable in Table 4 is the adjusted short-term debt ratio. We hypothesize that the adjusted short-term debt ratio is positively related to block ownership, indicating that the firm is less likely to underinvest. This association is expected to be
1
2
3
Constant Contracting market dummy
0.3414*** (6.08)
0.3607*** (5.16) −0.0466 −(0.40)
All block ownership Contracting market dummy
0.0088*** (15.58)
0.0091*** (12.82) −0.0007 −(0.60)
4
5
6
0.3485*** (6.21)
0.3683*** (5.27) −0.0488 −(0.42)
0.3607*** (6.10)
0.3485*** (5.24) −0.0582 −(0.49)
All outside block ownership Contracting market dummy
0.0072*** (10.76)
0.0076*** (8.92) −0.0009 −(0.67)
0.0071*** (10.73)
0.0076*** (8.91) −0.0009 −(0.68)
All inside block ownership Contracting market dummy
0.0114*** (13.87)
All non-manager inside block ownership Contracting market dummy
0.0109*** (10.60)
0.0113*** (8.99) −0.0010 −(0.46)
All managerial block ownership Contracting market dummy
0.0123*** (9.50)
0.0115*** (6.94) 0.0019 (0.70)
0.1122* (16.23)
0.1192*** (13.69) −0.0156 −(1.09) −0.6927*** −(12.18) 0.2899*** (3.36)
Control variables Firm size (sales) Contracting market dummy Financial Leverage ratio (total debt ratio) Contracting market dummy Adjusted R-squared Sample size
0.1123*** (16.28) −0.5738*** −(13.47) 0.0888 5543
0.1197*** (13.75) −0.0169 −(1.18) −0.6995*** −(12.35) 0.2863*** (3.33) 0.092 5543
0.1118*** (13.75) −0.5685*** −(13.36) 0.0921 5543
0.0114*** (11.11) 0.0002 (0.13)
0.1192*** (13.70) −0.0165 −(1.16) −0.6935*** −(12.26) 0.2847*** (3.32) 0.091 5543
−0.5685*** −(12.26) 0.094 5543
0.094 5543
The dependent variable is the proxy of asset management: asset turnover ratio. * , ** , *** indicate statistically significant at the 10%, 5%, and 1% levels, respectively. The dependent variable is asset turnover ratio, the proxy for asset management quality. Models 2, 4 and 6 is modifying Models 1, 3 and 5, adding market dummy interactive terms. Inside block ownership is defined as the sum of blocks held by (1) officer blockholders; (2) non-manager director blockholders; (3) affiliated blockholders; and (4) ESOP (Employees Share Ownership Plans)-related blockholders. Block ownership held by shareholders not in these four categories is defined as outside block ownership. Block ownership held by managerial blockholders is defined as managerial block ownership. The contracting market dummy is an interactive term, equal to the product of independent variables times a dummy variable, “1” for year 2000 and 2001 and “0” for other years. A significant interactive term indicates that this independent variable’s’ effect on the dependent variable is different between 1996–1999 and 2000–2001. For example, the coefficient of the block ownership reflects the magnitude of changes in the dependent variable due to the changes in this block ownership from1996 to1999. For year 2000–2001, the magnitude of the impact is the sum of the coefficient of the block ownership plus the coefficient of the contracting market dummy, the interactive term. The control variable, firm size is defined as the log of annual total sales; leverage is defined as ratio of debt to total assets. To control for industry effects, we classify the sample firms into 48 industries based upon their four digit SIC code, then estimate the panel data models with fixed effect for these 48 industries.
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Table 3 Asset management and blockholders
597
598
1 Constant Contracting market dummy All block ownership Contracting market dummy
2 ***
1.8427
(14.32)
−0.0028** −(2.17)
4 ***
1.7633 (11.07) 0.3043 (1.14)
***
1.8560
5 ***
(14.42)
1.7807 (11.17) 0.2930 (1.09)
−0.0051*** −(3.29)
−0.0040** −(2.02) −0.0025 −(0.80)
All inside block ownership Contracting market dummy
0.0009 (0.47)
All managerial block ownership Contracting market dummy
Adjusted R-squared Sample Size
−1.7777*** −(18.10) 0.0664 4671
(13.67)
1.6952*** (10.60) 0.2804 (1.04)
0.1074*** −0.1316*** −2.4187*** 1.4064*** 0.068 4671
(5.39) −(4.01) −(18.31) (7.16)
0.0522*** (3.29) −1.7610*** −(17.90) 0.0753 4671
−0.0054* −(3.51)
−0.0044** −(2.22) −0.0023 −(0.73)
−0.0074*** −(3.20)
−0.0048* −(1.70) −0.0072 −(1.48)
0.0154*** (5.11)
0.0163*** (4.17) −0.0036 −(0.58)
0.0023 (0.98) −0.0042 −(1.06)
All non-manager inside block ownership Contracting market dummy
0.0537*** (3.38)
1.7642
−0.0015 −(0.91) −0.0034 −(1.26)
All outside block ownership Contracting market dummy
Control variables Firm size (sales) Contracting market dummy Financial leverage ratio (total debt ratio) Contracting market dummy
6 ***
0.0522*** −0.1298*** −2.3968*** 1.3943*** 0.078 4671
(5.27) −(3.96) −(18.31) (7.09)
0.0601*** (3.79) −0.1266*** −(3.86) −1.6933*** −(17.17) 0.0793 4671
0.1116*** (5.60) −2.3226*** −(17.47) 1.3763*** (6.99) 0.087 4671
The dependent variable is the proxy of underinvestment: adjusted short-term debt ratio. * , ** , *** indicate statistically significant at the 10%, 5%, and 1% levels, respectively. The dependent variable is adjusted short-term debt ratio, the proxy for underinvestment. Models 2, 4 and 6 is modifying Model 1s, 3 and 5, adding market dummy interactive terms. Inside block ownership is defined as the sum of blocks held by (1) officer blockholders; (2) non-manager director blockholders; (3) affiliated blockholders; and (4) ESOP (Employees Share Ownership Plans)-related blockholders. Block ownership held by shareholders not in these four categories is defined as outside block ownership. Block ownership held by managerial blockholders is defined as managerial block ownership. The contracting market dummy is an interactive term, equal to the product of independent variables times a dummy variable, “1” for year 2000 and 2001 and “0” for other years. A significant interactive term indicates that this independent variable’s’ effect on the dependent variable is different between 1996–1999 and 2000–2001. For example, the coefficient of the block ownership reflects the magnitude of changes in the dependent variable due to the changes in this block ownership from1996 to1999. For year 2000–2001, the magnitude of the impact is the sum of the coefficient of the block ownership plus the coefficient of the contracting market dummy, the interactive term. The control variable, firm size is defined as the log of annual total sales; leverage is defined as ratio of debt to total assets. To control for industry effects, we classify the sample firms into 48 industries based upon their four digit SIC code, then estimate the panel data models with fixed effect for these 48 industries.
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Table 4 Underinvestment and blockholders
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more significant for managerial blockholders, as combinations of large ownership and managerial positions can particularly motivate officers to undertake riskier projects. As shown in Table 4, managerial block ownership is significantly positive related to the adjusted short-term debt ratio. Nevertheless, the coefficients of outside blockholders and non-manager inside blockholders are significantly negative. Following our prior argument, large firms are more likely to be scrutinized by financial analysts, so large firms tend to have fewer underinvestment problems than their smaller counterparts. As reported in Table 4, the coefficients of firm size are positive. However, when market moves from expanding to contracting, as shown in Model 6, the coefficient of firm size becomes slightly negative, −0.015 (=0.1116 − 0.1266).15 It is plausible that during a contracting market, large firms become more risk-averse and even forego good quality projects. Creditors may discipline managerial usage of capital; the firm has to commit making debt payments and restrains cash usage. Consequently, more underinvestment is expected in a firm associated with a higher leverage ratio. As reported in Table 4, the statistically significant and negative coefficients of financial leverage lend support to our prediction that higher financial leverage firms tend to have more underinvestment problems than their counterparts. 5. Conclusions Agency costs, such as managerial extravagance, poor asset management, and underinvestment, may arise when interests of managers conflict with those of shareholders. In this study, we use block ownership data to re-examine the effectiveness of blockholders concerning the reduction of these three types of agency costs during 1996–2001, a period that began with an expanding market and ended with a contracting market. Overall, our study reveals several important findings. First, our proxy for managerial extravagance, the SG&A ratio, is negatively related to block ownership. We also report that the proxy for asset management quality, the asset turnover ratio, is positively related to block ownership. Our findings suggest that blockholders effectively control managerial extravagance and facilitate asset utilization. Second, and most important, our evidence indicates that the effectiveness of blockholders differs depending on the types of block ownership and the nature of agency costs. Our evidence suggests that that outside blockholders are more vigilant about mitigating managerial extravagance while inside blockholders, especially managerial blockholders, are more effective when it comes to improving firm asset efficiency. Third, our proxy for underinvestment is the adjusted short-term debt ratio, with a larger ratio indicating that a firm is less likely to be involved in underinvestment. Our empirical results indicate that only managerial block ownership is positively related to the adjusted short-term ratio. This finding suggests that managerial blockholders significantly overcome underinvestment problems, which may be attributable to their duality roles. Fourth, our findings are robust throughout an expanding market condition to a contracting market. Taken together, our evidence suggests that outside and inside blockholders exert their interventions differently to mitigate the extent of three types of conflicts of interest between managers and shareholders.
15 The similar findings are also reported in Model 2 and Model 4 when the contracting market conditions are taken into account.
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Appendix A • Model 1: Agency Cost = α0 + α1 All Block + α2 FirmSize + α3 Leverage + IndustryDummies + ε • Model 2: Agency Cost = α0 + α1 ContractingDummy + α2 All Block + α3 All Block ∗ ContractingDummy +α4 FirmSize + α5 FirmSize ∗ ContractingDummy + α6 Leverage +α7 Leverage ∗ ContractingDummy + IndustryDummies + ε • Model 3: Agency Cost = α0 + α1 Outside Block + α2 Inside Block + α3 FirmSize + α4 Leverage +IndustryDummies + ε • Model 4: Agency Cost = α0 + α1 ContractingDummy + α2 Outside Block+ α3 Outside Block ∗ ContractingDummy + α4 Inside Block + α5 Inside Block ∗ ContractingDummy +α6 FirmSize + α7 FirmSize ∗ ContractingDummy + α8 Leverage+ α9 Leverage ∗ ContractingDummy + IndustryDummies + ε • Model 5: Agency Cost = α0 + α1 Outside Block + α2 NonManager Inside Block +α3 Manager Inside Block + α4 FirmSize + α5 Leverage + IndustryDummies + ε • Model 6 Agency Cost = α0 + α1 ContractingDummy + α2 Outside Block +α3 Outside Block ∗ ContractingDummy + α4 NonManager Inside Block +α5 NonManager Inside Block ∗ ContractingDummy + α6 Manager Inside Block +α7 Manager Inside Block ∗ ContractingDummy + α8 FirmSize +α9 FirmSize ∗ ContractingDummy + α10 Leverage + α11 Leverage ∗ ContractingDummy +IndustryDummies + ε
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References Allen, J. W., & Phillips, G. M. (2000). Corporate equity ownership, strategic alliances, and product market relationships. Journal of Finance, 55, 2791–2816. Anderson, R. C., & Lee, D. S. (1997). Ownership studies: Data source does matter. Journal of Financial and Quantitative Analysis, 32, 311–329. Ang, J., Cole, R., & Lin, J. (2000). Agency cost and ownership structure. Journal of Finance, 55, 81–106. Bethel, J., Libeskind, J., & Opler, T. (1998). Block share repurchase and corporate performance. Journal of Finance, 53, 605–634. Brickley, J. A., Lease, R. C., & Smith, C. (1994). Corporate voting: Evidence from charter amendment proposals. Journal of Corporate Finance, 3, 5–31. Bryan, S., Nash, R., & Patel, A. (2006). Can the agency costs of debt and equity explain the changes in executive compensation during the 1990s? Journal of Corporate Finance, 12, 516–535. Crutchley, C. E., & Hansen, R. S. (1989). A test of the agency theory of managerial ownership, corporate leverage, and corporate dividends. Financial Management, 18, 36–46. Demsetz, H., & Lehn, K. (1985). The structure of corporate ownership: Causes and consequences. Journal of Political Economy, 93, 1155–1177. Dlugosz, J., Fahlenbrach, R., Gompers, P., & Metrick, A. (2006). Large blocks of stock: Prevalence, size and measurement. Journal of Corporate Finance, 12(no. 3), 594–618. Fama, E., & Jensen, M. D. (1983). Separation of ownership and control. Journal of Law & Economics, 26, 301–325. Grossman, S., & Hart, O. D. (1982). Corporate financial structure and managerial incentives. The economic of information uncertainty. Chicago, IL: University of Chicago Press., pp. 102–137 Holderness, G. (April 2003). A survey of blockholders and corporate control. Economic Policy Review, 51–64. Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. American Economic Review, 76, 323–339. Jensen, M. C., & Meckling, M. H. (1976). Theory of the firm: Managerial behavior, agency costs, and ownership structure. Journal of Financial Economics, 3, 305–360. La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (1999). Corporate ownership around the world. Journal of Finance, 54, 471–517. Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. Journal of Finance, 52, 737–783. Singh, M., & Davidson, W. (2003). Agency cost, ownership structure and corporate governance mechanisms. Journal of Banking & Finance, 27, 793–816. Smith, M. (1996). Shareholder activism by institutional investors: Evidence from CalPERS. Journal of Finance, 51, 227–252. Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics, 40, 185–211.
Re-measuring agency costs: The effectiveness of blockholders Xiaoying (Cindy) Chen ∗ , Jasmine Yur-Austin Department of Finance, California State University, Long Beach, United States Received 1 April 2007; accepted 6 September 2007 Available online 5 October 2007
Abstract This study examines the effectiveness of blockholders in mitigating agency costs such as managerial extravagance, poor asset management and underinvestment. Our evidence suggests that outside and inside blockholders exert their interventions differently. We find that outside blockholders are more vigilant about mitigating managerial extravagance while inside blockholders, especially managerial blockholders, are more effective in improving the efficiency of firm asset utilization. However, only managerial blockholders significantly overcome underinvestment problems, which may be attributable to their duality roles. Published by Elsevier B.V. on behalf of the Board of Trustees of the University of Illinois. JEL classification: G30; G32 Keywords: Blockholders; Agency costs of equity; Managerial extravagance; Underinvestment; Contracting market
1. Introduction Jensen and Meckling (1976) define an agency problem as a situation in which managers (“agents”) take actions that contravene shareholders’ (“principals”) interests. Furthermore, mounting evidence in recent years suggests that managerial agency problems hamper the growth and the value of firms. A record number of top corporate executives face serious incarceration for their alleged financial crimes, such as presiding over massive corporate fraud and conducting illegal insider trading.1 Consequently, re-measuring agency costs and the effectiveness of corporate governance mechanisms deserves more academic study. ∗
Corresponding author. Tel.: +1 562 985 5072; fax: +1 562 985 1754. E-mail address: [email protected] (X. Chen). 1 Some famous examples are Ex-CEO of Tyco International executives Dennis Kozlowski and Mark Swartz, who were sentenced from 8 to 25 years in prison for raiding Tyco of hundreds of millions of dollars to pay for their extravagant 1062-9769/$ – see front matter . Published by Elsevier B.V. on behalf of the Board of Trustees of the University of Illinois.
doi:10.1016/j.qref.2007.09.003
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Traditionally, financial economists have suggested that ownership structure tends to mitigate the conflicts of interest between shareholders and managers, as well as other governance mechanisms such as executive compensation structure, board directors’ composition, and the threat of a hostile takeover (e.g., Shleifer & Vishny, 1997; Yermack, 1996). In this study, we are particularly interested in re-examining the effectiveness of blockholders in mitigating agency costs.2 Considerable prior empirical evidence suggests that concentrated shareholders (i.e., large blockholders) seem to influence managerial decisions. For example, Brickley, Lease, & Smith (1994) and Demsetz and Lehn (1985) suggest that large shareholders are likely to reduce the magnitude of managerial expropriation. Large shareholders may control the corporate board and select the company’s top managers. Hence, the critical role of blockholders in corporate governance should not be underestimated. Our analysis of blockholders in this study is based on dataset reported by Dlugosz, Fahlenbrach, Gompers, & Metrick (2006) (hereforth DFGM). Companies disclose their blockholders in their proxy statements. Due to the difficulty of collecting data directly from proxy statements, most prior studies use Compact Disclosure Database to identify large shareholders along with their shareholdings (e.g., Anderson & Lee, 1997). Nevertheless, DFGM disclose that Compact Disclosure Database fails to record footnotes of some corporate annual proxy statements, which leads to double counting the same ownership or mislabeling preferred stockholdings as common stockholdings. DFGM argue that the errors in Compact Disclosure may be responsible for some insignificant empirical results reported in earlier studies pertaining to large shareholders.3 Accordingly, DFGM compare the Compact Disclosure Database with the original proxy statements and produce a valid blockholders dataset. Our study adopts DFGM’s dataset (2006), which is likely to produce less biased test results and thus improve the validity of our findings. This study examines three types of agency costs: managerial extravagance, poor asset management, and underinvestment, which may arise when managers are incompetent or pursue their own interests at the expense of shareholders’ well-being. The study addresses the following questions. First, can blockholders act as effective monitoring mechanisms in reducing the three types of previously mentioned agency costs? Second, do outside blockholders or inside blockholders act more effectively? That is, which group of blockholders is most effective monitoring each of the agency costs? Third, do managerial blockholders (blockholders who are managers themselves) serve as better monitoring mechanisms? Finally, does the monitoring effectiveness of blockholders change from an expanding market to a contracting market? Our study focuses on large publicly traded companies over a 5-year period (from 1996 to 2001). The most relevant paper to ours is Singh and Davidson (2003), which focuses on a randomly selected sample of 118 large firms for year 1992 and year 1994. With a larger sample over a longer time period, we find that overall, large shareholders can constrain the above agency problems. Different from the findings of Singh and Davidson (2003), we find that both outside and inside blockholders significantly reduce managerial extravagance and facilitate asset utilization. personal spending. Former WorldCom Chairman Bernard J. Ebbers was sentenced to 25 years in prison for scheming in an $11 billion accounting fraud. In addition, John J. Rigas, the founder of Adelphia Communications Corp., received 15 years in prison for stealing millions from the cable company for personal business loans, moreover hiding more than $2.3 billion in debt, and lying to shareholders. 2 Rules 13d-1 through 13d-7 of the Securities Exchange Act of 1934 provide legal definitions of blockholders as those who own at least 5% of a firm’s outstanding common shares. 3 DFGM suggests that incorrect blockholder data is problematic when the data are used as independent variables but are less problematic when used as dependent variables. Our study uses blockholder data as independent variables; therefore, the adoption of DFGM’s sample is essential to justify our findings.
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Particularly, outside blockholders are more efficient in reducing managerial discretionary expenses while inside blockholders, especially managerial blockholders, are more efficient in enhancing asset utilization. Finally, our findings indicate that managerial blockholders significantly attenuate the underinvestment problems. These results are robust from an expanding market (1996–1999) to a contracting market (2000–2001).4 The study is structured as follows. Section 2 defines the proxies of agency costs and states the hypotheses. Section 3 describes sample firms along with our empirical approach. Section 4 presents empirical evidence as well as the interpretations. Section 5 offers conclusions. 2. Hypotheses related to proxies of agency costs In this section, we discuss the expected association between block ownership and three practical measures of agency costs: SG&A ratio (for managerial extravagance), asset turnover ratio (for asset management quality) and adjusted short-term debt ratio (for likelihood of involving in underinvestment). 2.1. Block ownership and managerial extravagance Managerial extravagance represents excessive managerial discretionary spending that is detrimental to corporate earnings. Managers may seek personal benefits at the expense of shareholders’ expenses, e.g., excessive compensation packages and lavish perquisites such as posh offices and country club memberships. Blockholders, having larger stakes invested in the firm, have more incentives to monitor these managerial opportunistic behaviors than smaller shareholders. When managerial extravagance arises, blockholders can cast the deciding votes to exert extra pressure on managers to control their discretionary spending. This incentive is particular strong for outside blockholders, who recently have become more actively involved in corporate governance (e.g., Smith, 1996). In contrast, since managerial blockholders enjoy, to some extent, private and personal benefits of corporate control (e.g., Holderness, 2003) that are not shared with other shareholders, managerial blockholders may not have the same incentives as outside blockholders to curtail managerial spending. Singh and Davidson (2003) use the SG&A ratio (Selling, General, and Administrative expenses/Total Sales) to capture how vigilant managers are in controlling their discretionary spending.5 We expect that block ownership is negatively related to this ratio, with the association being more significant for outside block ownership. 2.2. Block ownership and poor asset management Shareholders hire managers as “agents” to preside over business operations and implement corporate strategies, expecting that managers can effectively deploy the firms’ assets in pursuing optimal investment results. However, managers may fail to maximize shareholders’ benefits because of managerial incompetence. As a result, blockholders have good reason and great incentive to monitor the managers’ business decisions. Hence, we hypothesize that a firm with large block ownership will make better decisions regarding the firm’s assets. This monitoring should 4 DJIA (Dow Jones Industrial Average) gained 124.68% within 1996–1999 but lost approximately 12.83% from 2000 to 2001. 5 SG&A represents expenses incurred in regular business operation such as accounting expenses, advertising expenses, and labor related expenses (including salary, pension, retirement, and other employee benefits).
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be more effective when ownership and management are combined (e.g., Demsetz & Lehn, 1985). Therefore, we also hypothesize that managerial blockholders will deploy corporate assets more effectively than outside blockholders do. In earlier studies (Ang, Cole, & Lin, 2000; Singh & Davidson, 2003), the asset turnover ratio (Total Sales/Total Assets) has been used to evaluate asset utilization efficiency. We expect the asset turnover ratio is positively related to block ownership; this is especially significant for managerial block ownership. 2.3. Block ownership and underinvestment Underinvestment refers to the situation where a firm forgoes positive net present value projects. While shareholders would like managers to exploit a firm’s potential as much as possible by undertaking riskier projects, managers who have most of their human capital at risk with their current employers may prefer to invest in safer projects. Thus, blockholders would like to exert their influence by restraining any potential underinvestment possibilities. Bryan, Nash, & Patel (2006) propose using the adjusted short-term debt ratio (Market value of equity/Book value of equity multiplied by the Short-term debt ratio) to measure the likelihood that a firm is engaged in underinvestment. Because short-term debt needs to be repaid (or rolled over) in a timely fashion, firms are prone to make any positive net present value investment decisions more quickly in an attempt to meet their financial obligations.6 Hence, a large portion of short-term debt in a capital structure is projected to reduce underinvestment potential, especially for a firm associated with high growth opportunities (high Market value of equity/Book value of equity). Extending their argument, we expect that the adjusted short-term debt ratio is positively related to block ownership. This association is likely to be more significant for managerial block ownership. 3. Sample firms description and empirical approach 3.1. Sample firms DFGM concentrate on large shareholder ownership of companies in IRRC (Investors Responsibility Research Center) from 1996 to 2001.7 Based on their definitions, we classify blockholders into two types: inside blockholders and outside blockholders. Inside blockholders consist of managerial blockholders, non-officer director blockholders, affiliated blockholders, and ESOP (Employees Share Ownership Plans)-related blockholders.8 The outside blockholders are those that are not defined in the above categories. Given that the characteristics of blockholders are different, we conjecture that the influence of each type of blockholders may vary concerning the reduction of managerial agency costs. Next, we match DFGM ownership data with COMPUSTAT Industrial Annual files. The latter will be used to calculate the agency costs ratios. There is a possible causality issue between own6 A short-term maturity strategy helps mitigate underinvestment even when the short-term debt is rolled over. The short-term debt is replaced with new debt, which will be repriced based upon the firm’s current investment performance. Thus, the intent of taking a positive net present value project will ease the concerns of the lenders on the solvency of new debt and further lower the interest costs. 7 DFGM (pp. 596–597, 2006) state that “the IRRC’s universe is drawn from the Standard & Poor’s (S&P) 500 as well as the annual lists of the largest corporations in the publications of Fortune, Forbes, and Business Week. It is about 1500 of the largest U.S. companies”. 8 An affiliated blockholder is the voting party, either a trust or any individual, who can be partially influenced by either officer or director.
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ership data and agency costs (e.g., Allen & Phillips, 2000; Bethel, Libeskind, & Opler, 1998). While blockholders might reduce agency costs, the inherent agency problems may also influence the investment decisions of large shareholders, and the ownership structure may change accordingly. Therefore, we carefully investigate when firms report their annual accounting statements and when firms file their proxy statements with the SEC. When we match block ownership from the DFGM dataset with accounting value from COMPUSTAT, we ensure that the proxy filing date is prior to accounting value disclosed date.9 By doing so, our dataset, to some extent, mitigates any potential causality issue and enables us to investigate the effect if block ownership leads to any reduced agency costs. 3.2. Descriptions of proxies of agency costs Table 1 reports the descriptive statistics for proxies of agency costs as well as block ownership. Statistics are presented both for the pooled sample and for each sample year. As shown in the table, while the means of agency cost measures (SG&A ratio, asset turnover ratio and adjusted short-term debt ratio) are stable from 1996 to 1999, they change significantly in 2000–2001. The pooled mean SG&A ratio for our sample is 0.25, which is slightly lower than Singh and Davidson (2003) who report an average SG&A ratio of 0.28. Our sample pooled mean asset turnover ratio (1.03) is also lower than the value (1.43) reported in Singh and Davidson (2003) and much lower than the value (4.76) presented in the study by Ang et al. (2000).10 Compared to Singh and Davidson (2003), our sample firms have a larger average size with sales of $4.8 billion and with total assets of $11.6 billion.11 This may explain why these two agency cost ratios have different values between our sample and that of Singh and Davidson (2003). The pooled mean of all block ownership is 23.40%, among which outside ownership is 16.27% and inside ownership is 7.13%. It indicates that all blockholders compromise about one-fourth of total shares outstanding and outside blockholders control the largest proportion of the blockholdings. Among different groups of inside blockholders, managerial blockholders hold 2.75%, while the non-manager blockholders (affiliated blockholders, non-officer director blockholders, and ESOP-related blockholders) count 4.39%. 3.3. Multivariate regression models Our main interest is the relation between the three types of agency costs (managerial extravagance, poor asset management and underinvestment) and block ownership. Many prior studies (e.g., Crutchley & Hansen, 1989; Jensen, 1986) have reported that firm size and leverage ratio have bearings on the magnitude of agency costs. Therefore, we incorporate these two factors into our multivariate analysis. Large firms usually draw more scrutiny from the capital market, which 9 The SEC requires that all beneficial owners of more than 5% of a company’s common stock should be listed in the company’s proxy statement (DEF14A). The filing date with the SEC may occur in any month of a year. In COMPUSTAT, fiscal years of accounting data ending January 1 through May 31 are treated as ending in the prior calendar year. Take the example of Oracle Corporation, whose fiscal year ends in May. Hence, its accounting value disclosed in May 1996 will be treated as annual data in fiscal year 1995. Because Oracle Corporation files its proxy with SEC on September 11, 1996, we should match this proxy (in September 1996) with accounting value disclosed in May 1997 (which is fiscal year 1996). 10 Unlike our study, Ang et al. (2000) obtain their sample firms from the Federal Reserve Board’s National Survey of Small Business Finances (NSSBF). 11 Singh and Davidson (2003) report the sample firms with total sales of $2.1 billion and total asset of $2.0 billion.
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Table 1 Sample firm characteristic descriptive statistics Pooled
1996
1997
1998
1999
2000
2001
SG&A ratio
Mean S.T.D N
0.25 0.33 4430
0.2 0.14 584
0.23 0.19 565
0.24 0.20 805
0.23 0.16 781
0.27 0.30 842
0.29 0.62 853
Assets turnover ratio
Mean STD N
1.03 0.77 5567
1.08 0.780 745
1.04 0.76 727
1.05 0.76 1022
1.06 0.78 983
1.01 0.78 1045
0.97 0.74 1045
Adjusted short-term debt ratio
Mean
1.18
1.17
1.20
1.42
1.30
1.13
0.87
STD N
1.57 4675
1.22 608
1.50 600
2.12 861
1.85 842
1.40 879
0.87 885
Mean STD N
23.40 18.05 5572
20.67 17.16 746
20.87 17.17 727
23.76 18.43 1022
24.59 18.47 985
24.77 18.11 1046
24.27 18.11 1046
Mean
16.27
13.81
14.01
13.81
17.40
17.49
17.25
15.26 5572
13.89 746
14.03 727
15.43 1022
16.09 985
15.56 1046
15.38 1046
7.13
6.86
6.86
7.45
7.19
7.28
7.01
11.82 727
12.74 1022
12.31 985
11.93 1046
12.40 1046
Block ownership (%)
Outside block ownership (%)
STD N Inside block ownership (%)
Mean STD N
Non-manager inside block ownership (%)
Mean
12.26 5572
12.2612 746
4.39
4.56
4.57
4.36
4.20
4.34
4.39
9.74 5572
10.10 746
9.72 727
9.95 1022
9.32 985
9.19 1046
10.19 1046
2.75
2.30
2.29
3.09
3.00
2.94
2.62
STD N
7.76 5572
7.18 746
7.28 727
8.21 1022
8.24 985
7.95 1046
7.37 1046
Total assets ($ millions)
Mean STD N
11632.34 51323.34 5572
9383.21 32308.54 746
11178.75 44390.65 727
9454.43 39911.26 1022
111178.7594 12377.03 59419.50 57211.29 985 1046
13252.69 61325.17 1046
Total sales ($ millions)
Mean STD N
4805.78 11509.25 5569
4395.44 8966.18 745
4938.61 10230.07 727
4227.00 9866.69 1022
5130.02 11759.94 984
5045.71 13122.72 1046
5026.49 13339.45 1045
Total leverage
Mean STD N
0.59 0.24 5550
0.60 0.21 740
0.60 0.2 725
0.59 0.23 1017
0.59 0.23 981
0.59 0.29 1043
0.60 0.24 1044
STD N Managerial block ownership (%)
Mean
The sample used in this study consists of 1371 firms in Dlugosz, Fahlenbrach, Gompers and Metrick (2006) (hereforth DFGM) from 1996 to 2001. We match the block ownership data in DFGM to accounting data obtained from COMPUSTAT. SG&A ratio = Selling, General, and Administrative expense/Total sales; Asset turnover ratio = Total sales/Total assets; Adjusted shortterm debt ratio = [(Total assets − Book value of equity + Market value of equity)/Total assets]*(Short-term debt/Total debt); Block ownership = Percentage held by all blockholders for that firm-year; Outside block ownership = Percentage held by all outside blockholders; Non-manager inside block ownership = Percentage held by all affiliated blockholders, by all ESOP-related blockholders and by all non-officer director blockholders; Managerial block ownership = Percentage held by all managerial blockholders; Inside block ownership = Non-manager inside block ownership + Managerial block ownership; Total leverage = Total debt/Total assets.
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can be regarded as an external monitoring mechanism. Thus, it is often expected that the firm size and agency costs are negatively related. We measure the firm size by the natural log of a firm’s annual sales. As shown in Table 1, total sales are associated with extremely high standard deviations; it indicates a wide range of total sales values. We follow a common rescaling approach by taking logarithms of the original sales values. A caveat of this methodology is that it becomes difficult to quantify the relation between dependent variables and total sales. Since the purpose of this study is not to measure the magnitude in agency costs given changes in firm size, we use logarithmic values in our regression analysis. Creditors are also important stakeholders. As leverage increases, a firm’s default risk increases; thus, creditors have incentives to monitor managerial performance. We include total leverage (total debt ratio) as a control variable reflecting the extent of creditors’ incentives of serving as an external governance mechanism. Past research (e.g., Fama & Jensen, 1983; La Porta, Lopez-de-Silanes, & Shleifer, 1999) suggests that the effectiveness of monitoring or binding mechanisms arguably depends on exogenous variables, such as market conditions and industrial sectors. Accordingly, we add a contracting market dummy for years 2000 and 2001, and adopt interaction terms by multiplying explanatory variables with the contracting market dummy to indicate any variations in agency costs due to market conditions. Further, we define 48 industries according to COMPUTSTAT 4-digit SIC codes.12 Rather than using a simple pooled regression model, we employ a panel data model with fixed effects, taking into account magnitudes of agency costs attributable to any industry effects. This fixed effect model would have constant slopes but intercepts that differ according to industries.13 4. Empirical evidence and interpretations In this section, we present the results of our multivariate regression analysis relating agency cost proxies to block ownership, taking into account the controlled firm size, leverage, industry classification, and market conditions. Model 1 examines the relationship between agency costs and the total block ownership. Model 2 incorporates the contracting market effect to Model 1. Model 3 examines the impacts of outside block ownership and inside block ownership on agency costs. Model 4, modifying Model 3, encompasses the contracting market effect. Model 5 further examines how the outside block ownership, managerial block ownership, and non-manager block ownership relate to agency costs. Model 6, modifying Model 5, encompasses the contracting market effect. All of the six models are estimated by panel data regressions with fixed effects.14 4.1. Agency cost—managerial extravagance The dependent variable in Table 2 is the SG&A ratio, used as a proxy of managerial extravagance. Model 1 reports a significant negative relation (−0.0017, t = −5.94) between all block ownership and SG&A ratio. This relation is consistent when various combinations of contracting market dummies and the independent variables are added in Model 2. Model 3 reports similar findings, which show that both outside (−0.0019, t = −5.67) and inside block ownership (−0.0013, t = −3.33) are significantly negative related to SG&A expenditure. These relations are consistent 12 The industry definition can be found at Ken French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ data library.html. 13 We also ran a panel model with fixed effects for firms and obtained identical results. 14 Equations of all six models are presented in Appendix.
1
2
3
Constant Contracting market dummy
0.7185*** (25.85)
0.7549*** (21.52) −0.0941 −(1.64)
All block ownership Contracting market dummy
−0.0017*** −(5.94)
−0.0017*** −(4.72) 0.0000 −(0.01)
4
5
6
0.7200*** (25.89)
0.7567*** (21.54) −0.0947 −(1.64)
0.7176*** (25.69)
0.7537*** (21.34) −0.0931 −(1.64)
All outside block ownership Contracting market dummy
−0.0019*** −(5.67)
−0.0019*** −(4.46) 0.0000 (0.01)
−0.0019*** −(5.71)
−0.0020*** −(4.50) 0.0000 (0.03)
All inside block ownership Contracting market dummy
−0.0013*** −(3.33)
−0.0014*** −(2.72) 0.0000 (0.06)
All non-manager inside block ownership Contracting market dummy
−0.0016*** −(3.23)
−0.0016*** −(2.72) 0.0001 (0.14)
All managerial block ownership Contracting market dummy
−0.0009 −(1.41)
−0.0008−(1.00) −0.0002 −(0.18)
−0.0563*** −(16.47)
−0.0611*** −(14.10) 0.0125* (1.77) −0.0883** −(2.43) 0.0445 (0.75)
Control variables Firm size (sales) Contracting market dummy Financial Leverage ratio (total debt ratio) Contracting market dummy Adjusted R-squared Samplesize
−0.0565*** −(16.60) −0.0769*** −(2.70) 0.0637 4409
−0.0614*** −(14.23) 0.0126* (1.79) −0.0951*** −(2.64) 0.0462 (0.78) 0.064 4409
−0.0566*** −(16.63) −0.0734** −(2.56) 0.0643 4409
−0.0615*** −(14.25) 0.0126* (1.80) −0.0916** −(2.53) 0.0463 (0.78) 0.065 4409
−0.0707** −(2.45) 0.0651 4409
0.065 4409
The dependent variable is the proxy of managerial extravagance: SG&A ratio. * , ** , *** indicate statistically significant at the 10%, 5%, and 1% levels, respectively. The dependent variable is SG&A expense ratio, the proxy for managerial extravagance. Models 2, 4 and 6 is modifying Models 1, 3 and 5, adding market dummy interactive terms. Inside block ownership is defined as the sum of blocks held by (1) managerial blockholders; (2) non-manager director blockholders; (3) affiliated blockholders; and (4) ESOP (Employees Share Ownership Plans)-related blockholders. Block ownership held by shareholders not in these four categories is defined as outside block ownership. Block ownership held by managerial blockholders is defined as managerial block ownership. The contracting market dummy is an interactive term, equal to the product of independent variables times a dummy variable, “1” for year 2000 and 2001 and “0” for other years. A significant interactive term indicates that this independent variable’s’ effect on the dependent variable is different between 1996–1999 and 2000–2001. For example, the coefficient of the block ownership reflects the magnitude of changes in the dependent variable due to the changes in this block ownership from1996 to1999. For years 2000 to 2001, the magnitude of the impact is the sum of the coefficient of the block ownership plus the coefficient of the contracting market dummy, the interactive term. The control variable, firm size is defined as the log of annual total sales; leverage is defined as ratio of debt to total assets. To control for industry effects, we classify the sample firms into 48 industries based upon their four digit SIC code, then estimate the panel data models with fixed effect for these 48 industries.
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Table 2 Managerial extravagance and blockholders
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after contracting market effect variables are incorporated in Model 4. Accordingly, these results support the managerial extravagance hypothesis that blockholders’ monitoring influences managers to significantly curtail their discretionary spending. In contrast to Singh and Davidson (2003) who suggest outside blockholders have no significant effect on reducing managerial spending, our study finds that both outside and inside blockholders have significant influence of reducing managerial extravagance. Further, our evidence suggests that outside blockholders are more efficient in cutting the expenses than inside blockholders. We also investigate whether managerial blockholders exert their duality role to further control SG&A spending. Models 5 and 6 indicate that while outside blockholders and other inside blockholders significantly decrease such spending, managerial blockholders have an insignificant influence on it. This is consistent with Holderness’s (2003) argument that in some situations, managers’ private benefits of corporate control outweigh share benefits of control. Both of the external monitoring variables of firm size and leverage are negatively and significantly associated with the SG&A ratio in all six models, consistent with prior studies (e.g., Grossman & Hart, 1982; Singh & Davidson, 2003). Because larger firms draw more public attention and extensive analyst coverage and creditors restrain managers from accessing extra cash flow, discretionary spending is lower in larger firms and in firms with a high leverage ratio. 4.2. Agency cost—poor asset management In Table 3, the dependent variable is the asset turnover ratio which is a proxy for asset management quality. The six models present the same results that the coefficients of block ownerships are positive and significant. Taken together, the evidence implies that blockholders improve asset management quality, and inside blockholders, especially managerial blockholders, are more efficient. These findings are consistent with our asset management hypothesis and the results are robust after considering market conditions. Consistent with the Singh and Davidson (2003) study, the coefficients of firm size are significantly positive. It suggests that a large firm tends to capture more synergy gains throughout its business lines; hence, asset turnover efficiency tends to improve as a firm gets larger. The leverage ratio is significantly negatively related to asset turnover ratio. Noticeably, as shown in Model 6, the coefficient of leverage ratio becomes less negative from −0.6927 to −0.4028 (=−0.6927 + 0.2899) when the economy is contracting. It is possible that the creditors’ desire to improve asset management nevertheless outweighs a free-rider problem. When the economy is expanding, the prospects of the business are good; the creditors feel less need to supervise managers’ performance or to question their strategic decisions. Consequently, creditors tend to rely more on shareholders for monitoring management. However, when the economy is slowing down, the possibility of corporate financial distress increases and the firm’s default risk increases. Under this situation, creditors have more incentives to monitor a manager’s asset management effectiveness (e.g., Ang et al., 2000). 4.3. Agency cost—underinvestment The results concerning the relation between underinvestment and blockholders are presented in Table 4. According to Bryan et al. (2006), a firm with a high adjusted short-term debt ratio is less likely to forgo profitable projects. The dependent variable in Table 4 is the adjusted short-term debt ratio. We hypothesize that the adjusted short-term debt ratio is positively related to block ownership, indicating that the firm is less likely to underinvest. This association is expected to be
1
2
3
Constant Contracting market dummy
0.3414*** (6.08)
0.3607*** (5.16) −0.0466 −(0.40)
All block ownership Contracting market dummy
0.0088*** (15.58)
0.0091*** (12.82) −0.0007 −(0.60)
4
5
6
0.3485*** (6.21)
0.3683*** (5.27) −0.0488 −(0.42)
0.3607*** (6.10)
0.3485*** (5.24) −0.0582 −(0.49)
All outside block ownership Contracting market dummy
0.0072*** (10.76)
0.0076*** (8.92) −0.0009 −(0.67)
0.0071*** (10.73)
0.0076*** (8.91) −0.0009 −(0.68)
All inside block ownership Contracting market dummy
0.0114*** (13.87)
All non-manager inside block ownership Contracting market dummy
0.0109*** (10.60)
0.0113*** (8.99) −0.0010 −(0.46)
All managerial block ownership Contracting market dummy
0.0123*** (9.50)
0.0115*** (6.94) 0.0019 (0.70)
0.1122* (16.23)
0.1192*** (13.69) −0.0156 −(1.09) −0.6927*** −(12.18) 0.2899*** (3.36)
Control variables Firm size (sales) Contracting market dummy Financial Leverage ratio (total debt ratio) Contracting market dummy Adjusted R-squared Sample size
0.1123*** (16.28) −0.5738*** −(13.47) 0.0888 5543
0.1197*** (13.75) −0.0169 −(1.18) −0.6995*** −(12.35) 0.2863*** (3.33) 0.092 5543
0.1118*** (13.75) −0.5685*** −(13.36) 0.0921 5543
0.0114*** (11.11) 0.0002 (0.13)
0.1192*** (13.70) −0.0165 −(1.16) −0.6935*** −(12.26) 0.2847*** (3.32) 0.091 5543
−0.5685*** −(12.26) 0.094 5543
0.094 5543
The dependent variable is the proxy of asset management: asset turnover ratio. * , ** , *** indicate statistically significant at the 10%, 5%, and 1% levels, respectively. The dependent variable is asset turnover ratio, the proxy for asset management quality. Models 2, 4 and 6 is modifying Models 1, 3 and 5, adding market dummy interactive terms. Inside block ownership is defined as the sum of blocks held by (1) officer blockholders; (2) non-manager director blockholders; (3) affiliated blockholders; and (4) ESOP (Employees Share Ownership Plans)-related blockholders. Block ownership held by shareholders not in these four categories is defined as outside block ownership. Block ownership held by managerial blockholders is defined as managerial block ownership. The contracting market dummy is an interactive term, equal to the product of independent variables times a dummy variable, “1” for year 2000 and 2001 and “0” for other years. A significant interactive term indicates that this independent variable’s’ effect on the dependent variable is different between 1996–1999 and 2000–2001. For example, the coefficient of the block ownership reflects the magnitude of changes in the dependent variable due to the changes in this block ownership from1996 to1999. For year 2000–2001, the magnitude of the impact is the sum of the coefficient of the block ownership plus the coefficient of the contracting market dummy, the interactive term. The control variable, firm size is defined as the log of annual total sales; leverage is defined as ratio of debt to total assets. To control for industry effects, we classify the sample firms into 48 industries based upon their four digit SIC code, then estimate the panel data models with fixed effect for these 48 industries.
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Table 3 Asset management and blockholders
597
598
1 Constant Contracting market dummy All block ownership Contracting market dummy
2 ***
1.8427
(14.32)
−0.0028** −(2.17)
4 ***
1.7633 (11.07) 0.3043 (1.14)
***
1.8560
5 ***
(14.42)
1.7807 (11.17) 0.2930 (1.09)
−0.0051*** −(3.29)
−0.0040** −(2.02) −0.0025 −(0.80)
All inside block ownership Contracting market dummy
0.0009 (0.47)
All managerial block ownership Contracting market dummy
Adjusted R-squared Sample Size
−1.7777*** −(18.10) 0.0664 4671
(13.67)
1.6952*** (10.60) 0.2804 (1.04)
0.1074*** −0.1316*** −2.4187*** 1.4064*** 0.068 4671
(5.39) −(4.01) −(18.31) (7.16)
0.0522*** (3.29) −1.7610*** −(17.90) 0.0753 4671
−0.0054* −(3.51)
−0.0044** −(2.22) −0.0023 −(0.73)
−0.0074*** −(3.20)
−0.0048* −(1.70) −0.0072 −(1.48)
0.0154*** (5.11)
0.0163*** (4.17) −0.0036 −(0.58)
0.0023 (0.98) −0.0042 −(1.06)
All non-manager inside block ownership Contracting market dummy
0.0537*** (3.38)
1.7642
−0.0015 −(0.91) −0.0034 −(1.26)
All outside block ownership Contracting market dummy
Control variables Firm size (sales) Contracting market dummy Financial leverage ratio (total debt ratio) Contracting market dummy
6 ***
0.0522*** −0.1298*** −2.3968*** 1.3943*** 0.078 4671
(5.27) −(3.96) −(18.31) (7.09)
0.0601*** (3.79) −0.1266*** −(3.86) −1.6933*** −(17.17) 0.0793 4671
0.1116*** (5.60) −2.3226*** −(17.47) 1.3763*** (6.99) 0.087 4671
The dependent variable is the proxy of underinvestment: adjusted short-term debt ratio. * , ** , *** indicate statistically significant at the 10%, 5%, and 1% levels, respectively. The dependent variable is adjusted short-term debt ratio, the proxy for underinvestment. Models 2, 4 and 6 is modifying Model 1s, 3 and 5, adding market dummy interactive terms. Inside block ownership is defined as the sum of blocks held by (1) officer blockholders; (2) non-manager director blockholders; (3) affiliated blockholders; and (4) ESOP (Employees Share Ownership Plans)-related blockholders. Block ownership held by shareholders not in these four categories is defined as outside block ownership. Block ownership held by managerial blockholders is defined as managerial block ownership. The contracting market dummy is an interactive term, equal to the product of independent variables times a dummy variable, “1” for year 2000 and 2001 and “0” for other years. A significant interactive term indicates that this independent variable’s’ effect on the dependent variable is different between 1996–1999 and 2000–2001. For example, the coefficient of the block ownership reflects the magnitude of changes in the dependent variable due to the changes in this block ownership from1996 to1999. For year 2000–2001, the magnitude of the impact is the sum of the coefficient of the block ownership plus the coefficient of the contracting market dummy, the interactive term. The control variable, firm size is defined as the log of annual total sales; leverage is defined as ratio of debt to total assets. To control for industry effects, we classify the sample firms into 48 industries based upon their four digit SIC code, then estimate the panel data models with fixed effect for these 48 industries.
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Table 4 Underinvestment and blockholders
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599
more significant for managerial blockholders, as combinations of large ownership and managerial positions can particularly motivate officers to undertake riskier projects. As shown in Table 4, managerial block ownership is significantly positive related to the adjusted short-term debt ratio. Nevertheless, the coefficients of outside blockholders and non-manager inside blockholders are significantly negative. Following our prior argument, large firms are more likely to be scrutinized by financial analysts, so large firms tend to have fewer underinvestment problems than their smaller counterparts. As reported in Table 4, the coefficients of firm size are positive. However, when market moves from expanding to contracting, as shown in Model 6, the coefficient of firm size becomes slightly negative, −0.015 (=0.1116 − 0.1266).15 It is plausible that during a contracting market, large firms become more risk-averse and even forego good quality projects. Creditors may discipline managerial usage of capital; the firm has to commit making debt payments and restrains cash usage. Consequently, more underinvestment is expected in a firm associated with a higher leverage ratio. As reported in Table 4, the statistically significant and negative coefficients of financial leverage lend support to our prediction that higher financial leverage firms tend to have more underinvestment problems than their counterparts. 5. Conclusions Agency costs, such as managerial extravagance, poor asset management, and underinvestment, may arise when interests of managers conflict with those of shareholders. In this study, we use block ownership data to re-examine the effectiveness of blockholders concerning the reduction of these three types of agency costs during 1996–2001, a period that began with an expanding market and ended with a contracting market. Overall, our study reveals several important findings. First, our proxy for managerial extravagance, the SG&A ratio, is negatively related to block ownership. We also report that the proxy for asset management quality, the asset turnover ratio, is positively related to block ownership. Our findings suggest that blockholders effectively control managerial extravagance and facilitate asset utilization. Second, and most important, our evidence indicates that the effectiveness of blockholders differs depending on the types of block ownership and the nature of agency costs. Our evidence suggests that that outside blockholders are more vigilant about mitigating managerial extravagance while inside blockholders, especially managerial blockholders, are more effective when it comes to improving firm asset efficiency. Third, our proxy for underinvestment is the adjusted short-term debt ratio, with a larger ratio indicating that a firm is less likely to be involved in underinvestment. Our empirical results indicate that only managerial block ownership is positively related to the adjusted short-term ratio. This finding suggests that managerial blockholders significantly overcome underinvestment problems, which may be attributable to their duality roles. Fourth, our findings are robust throughout an expanding market condition to a contracting market. Taken together, our evidence suggests that outside and inside blockholders exert their interventions differently to mitigate the extent of three types of conflicts of interest between managers and shareholders.
15 The similar findings are also reported in Model 2 and Model 4 when the contracting market conditions are taken into account.
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Appendix A • Model 1: Agency Cost = α0 + α1 All Block + α2 FirmSize + α3 Leverage + IndustryDummies + ε • Model 2: Agency Cost = α0 + α1 ContractingDummy + α2 All Block + α3 All Block ∗ ContractingDummy +α4 FirmSize + α5 FirmSize ∗ ContractingDummy + α6 Leverage +α7 Leverage ∗ ContractingDummy + IndustryDummies + ε • Model 3: Agency Cost = α0 + α1 Outside Block + α2 Inside Block + α3 FirmSize + α4 Leverage +IndustryDummies + ε • Model 4: Agency Cost = α0 + α1 ContractingDummy + α2 Outside Block+ α3 Outside Block ∗ ContractingDummy + α4 Inside Block + α5 Inside Block ∗ ContractingDummy +α6 FirmSize + α7 FirmSize ∗ ContractingDummy + α8 Leverage+ α9 Leverage ∗ ContractingDummy + IndustryDummies + ε • Model 5: Agency Cost = α0 + α1 Outside Block + α2 NonManager Inside Block +α3 Manager Inside Block + α4 FirmSize + α5 Leverage + IndustryDummies + ε • Model 6 Agency Cost = α0 + α1 ContractingDummy + α2 Outside Block +α3 Outside Block ∗ ContractingDummy + α4 NonManager Inside Block +α5 NonManager Inside Block ∗ ContractingDummy + α6 Manager Inside Block +α7 Manager Inside Block ∗ ContractingDummy + α8 FirmSize +α9 FirmSize ∗ ContractingDummy + α10 Leverage + α11 Leverage ∗ ContractingDummy +IndustryDummies + ε
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