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Abstract

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References

We empirically decompose private benefits into benefits accruing from ownership and benefits accruing from control. We document that private benefits increase slowly with respect to the ownership level but increase rapidly with respect to the blockholder's likelihood of exercising control. The decomposition of private benefits allows us to quantify the magnitude of nonpecuniary private benefits by examining the block premium when the blockholder's likelihood of exercising control is close to zero. We find that the size of nonpecuniary private benefits ranges from 0.61% to 5.92% of the share price, or 18% to 29% of the total private benefits.


I. Introduction

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References

The literature defines private benefits as benefits that accrue to managers or controlling shareholders, but not to minority shareholders. Given the importance of private benefits—in the sense that many issues in corporate governance arise from agency problems, and private benefits are at the heart of agency problems—numerous studies examine private benefits. The seminal articles of Jensen and Meckling (1976) and Demsetz and Lehn (1985) discuss private benefits and cite both pecuniary and nonpecuniary private benefits. Later, Barclay and Holderness (1989) find a way to quantify the size of private benefits by using block trade premium and reiterate that private benefits can be pecuniary or nonpecuniary. However, thus far, no study has come up with a means of estimating the magnitude of nonpecuniary private benefits. This article fills the gap by introducing a way of estimating the size of nonpecuniary private benefits by decomposing private benefits into an ownership component and a control component.

Because a controlling party will use corporate resources to his or her benefit only when it is difficult or impossible to prove these actions in court, private benefits are inherently difficult to measure. Despite these difficulties, Barclay and Holderness (1989) find a means of estimating private benefits using block premiums, which is measured by the difference between the price per share paid for the block of common stock and the market price of the stock following the announcement of the block transaction. If all shareholders receive benefits in proportion to their fractional ownership, blocks should trade at the exchange price. However, if blockholders can enjoy benefits that do not accrue to minority shareholders, blocks will trade at a premium to the postannouncement exchange price.1 Using a sample of 63 block trades during 1978–1982, Barclay and Holderness find that the block premium averages 16% of the postannouncement exchange price.

As noted by Jensen and Meckling (1976), Demsetz and Lehn (1985), and Barclay and Holderness (1989), private benefits can be either pecuniary or nonpecuniary. Pecuniary private benefits are private benefits that can be stated in monetary terms (e.g., excessive salary or the tunneling of the company's resources). Nonpecuniary private benefits are private benefits that cannot be stated in monetary terms (e.g., the pride of becoming a large owner; becoming part of the business network; interacting with influential businessmen, politicians, and celebrities; and enjoying the recognition, fame, and prestige that accompany one's heightened social status). However, because of their nature, nonpecuniary private benefits can evade easy measurement. In this study, we propose a way to measure the magnitude of nonpecuniary private benefits by noting that all pecuniary private benefits must involve some control of the firm. For example, the tunneling of the company's resources involves the exercise of control in the company. Therefore, we estimate the level of nonpecuniary private benefits by examining the size of private benefits when the blockholder's likelihood of exercising control in company is zero (or very close to zero). This can be achieved if we can decompose the sources of private benefits into those that stem from the exercise of control and those that stem from having just ownership of the company.

Private benefits of ownership are benefits that one gets by just owning a block of shares. These benefits are something other than benefits from a claim on future cash flows because cash flows accrue to all shareholders and therefore are not private benefits. The aforementioned examples of nonpecuniary private benefits do not accrue to minority shareholders, and the blockholder can enjoy these benefits without the need to exert control in the company's decision-making process. Therefore, these benefits represent private benefits of ownership.

To decompose private benefits into those arising from ownership and those arising from control, we use data on block trades. Block trades are a good source of data for this research because, following Barclay and Holderness (1989), we can estimate private benefits by calculating the block premium that is associated with a block trade. Also, we can decompose private benefits into ownership and control because blockholders are often powerful enough to exercise control in the company (Holderness and Sheehan 1988). Blockholders, in addition to owning different portions of a company's shares, vary in terms of how much control they exercise over the firm. For example, person A, who owns 10% of the shares of company X, may be very active in the firm's decision-making process, whereas person B, who holds 20% of the shares of company Y, may be passive in exercising control in the company.

Therefore, because every blockholder is unique with respect to his or her ownership level and control level, we can decompose private benefits into ownership and control.2 First, with regard to the ownership level, we use the percentage of shares acquired in the block trade. Then, we measure private benefits of ownership by the marginal effect of the percentage of shares acquired on the block premium. Second, with regard to measuring the control level, we estimate the likelihood of top executive turnover within one year following the block trade. This measure is created by first examining whether there was a top executive turnover within one year of the block transaction and then constructing an implied probability of top executive turnover at the time of the block trade. The assumption here is that the most significant control activity that a blockholder can exercise is to replace the top executive of the company; thus, control activities are best reflected by change in the top executive.3 Then, we measure private benefits of control by the marginal effect of the probability of top executive turnover on the block premium. The preceding tasks are achieved by using a two-stage regression model, where the top executive turnover variable is treated as an endogenous variable in explaining the dependent block premium variable.

We find that private benefits, as measured by the block premium, increase slowly with respect to the level of ownership but increase rapidly with respect to the blockholder's likelihood of exercising control in the company. (These relations are depicted later in Figure I.) However, we find that even when there is very small chance of exercising control in the company, investors are willing to pay a premium to become blockholders. The size of nonpecuniary private benefits ranges from 0.61% (for a 5% increase in ownership) to 5.92% (for a 50% increase in ownership) of the share price. The proportion of nonpecuniary private benefits to total private benefits ranges from 18% (for a 5% increase in ownership) to 29% (for a 50% increase in ownership). This shows that nonpecuniary private benefits comprise a nontrivial portion of total private benefits.

image

Figure I. Relation among Block Premium, Percentage of Shares Acquired, and Probability of Top Executive Turnover. This figure shows a three-dimensional plot that depicts the relation among block premiums, percentage of shares acquired, and probability of top executive turnover, according to model 5 of Table 4.

Download figure to PowerPoint

Our empirical method is not without limitations.4 First, the use of the likelihood of executive turnover for measuring the blockholder's expected level of control may not be perfect because there may be other, less significant, ways of exercising control in the company. If this is the case, our measure of ownership level will incorporate residual control activities that are not captured by the likelihood of top executive turnover. Therefore, we may be underestimating the control level and overestimating the ownership level. Second, as Dyck and Zingales (2004) note, the block premium estimate used in Barclay and Holderness (1989) can improperly estimate private benefits. There is no bias in the estimate only when the block trade price reflects the buyer's willingness to pay (i.e., the seller has all the bargaining power) or when security values (i.e., the benefit from cash-flow rights) are the same for the buyer and the seller. If that is not the case, the bias becomes greater as the seller's bargaining power becomes smaller.5 However, Dyck and Zingales find that in countries with lower levels of private benefits, the seller has greater bargaining power and that the United States shows one of the lowest levels of private benefits. Therefore, we think our sample of block trades from the United States is minimally affected by the bias of the block premium measure.

II. Data

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References

Data Formation

We collect block trade data over 1987–2005 from the Securities Data Company (SDC) Mergers and Acquisitions database. Transactions must involve the transfer of a block of shares that comprises between 5% (inclusive) and 50% (exclusive) of the shares outstanding and must be classified as “block purchase” in the acquisition technique category of the SDC Mergers and Acquisitions database. The lower cutoff point of 5% is used for our data set because it is the point that triggers a mandatory filing with the Securities and Exchange Commission (SEC) for the block transaction. The upper cutoff point is 50% because we want to examine how block premium changes with regard to different levels of control in the company, whereas if someone acquires 50% or more of a company's share, he or she will then have gained full control of the company. Therefore, because our sample consists of block trades that involve partial control of the company, it allows us to observe cross-sectional variation in the levels of ownership and control. This selection criterion also excludes cases where the final shareholding—after the block trade—of the new blockholder becomes 50% or more, as these block trades entail full control of the company.

From our initial sample size of 1,804 transactions, we follow the sample selection criteria used by Dyck and Zingales (2004). There must be information about the price paid per share for the block transaction and the exchange price one day after the announcement of the block trade. We exclude cases where the price paid per share may not be objectively valued, such as transactions involving convertible bonds, options, and warrants.

To rule out instances where the transaction price may not reflect private benefits, we exclude cases where the target or the acquirer is either a government agency or a subsidiary of the other party. We further exclude transactions that are open market repurchases, tender offers, spinoffs, recapitalizations, self-tenders, exchange offers, repurchases, and acquisition of remaining interest. Also, to stay away from block trades that are driven by takeover motives, we rule out block trades that happen within six months before a merger or acquisition that involves the block-trading company and block trades that are accompanied by an indication of either a takeover or a tender offer for the remaining shares, as inferred from reading the SDC synopsis.

Although the parties of the block transaction can be either insiders or outsiders of the company, Barclay and Holderness (1989) note that purchasers of the block are typically outsiders and not one of the firm's directors or officers. Given the objective of our article, we restrict our sample to block transactions where both parties are not affiliated with the company and where the block purchaser is not a current blockholder of the company. The reason behind this selection criterion is that it is unclear how accurately the block premium reflects private benefits when insiders or current blockholders take part in the transaction. For example, insiders or current blockholders who purchase a block of shares may already possess significant controlling power within the company, in which case they will not pay extra for the block. Similarly, in the case of insiders selling a block, they may retain control of the firm even after the trade and thus not worry about losing their private benefits. By focusing on new outside purchasers, we are able to conduct a cleaner measurement of private benefits.

We identify insider ownership and the percentage of outsiders among board members of the company whose block is traded. We collect these data from the firm's proxy statement with the most recent record date before the block transaction. We search the LexisNexis Company Profiles to identify top executive turnover within one year after the block transaction. A top executive is defined as the CEO or, if a firm has no CEO, the president. As in previous studies (e.g., Weisbach 1988; Denis, Denis, and Sarin 1997), we exclude from our sample cases where top executive turnover occurs either as part of the normal retirement process or as a result of death or illness. The criterion for normal retirement is that the turnover takes place between the ages of 64 and 66 for the top executive. With these criteria, the size of our final sample is 738.

Summary Statistics

Table 1 reports descriptive statistics for the 738 block trades sample and for subsamples of two categories of block trades: those followed by top executive turnover within one year of the block transaction and those that are not followed by top executive turnover within one year of the block trade.

Table 1. Summary Statistics.
VariablesWhole SampleSubsequent CEO TurnoverNo Subsequent CEO TurnoverDifference in Mean
MeanMedianMeanMedianMeanMedian
  1. Note: This table gives means and medians of several variables for 738 firms whose blocks are traded between 1987 and 2005. Firms belong to subsequent CEO turnover group if there is a turnover in the top executive position of the firm within one year of the block trade. Block premium (%) is defined as 100 ×{(price per share paid for the block) − (exchange price one day after the announcement of the transaction)}/(exchange price one day after the announcement of the transaction). Percent of shares acquired is the percentage of the firm's equity that is acquired in the block transaction. Transaction value is the number of shares acquired in the block transaction multiplied by the trading price of the block. Prior firm performance is the percentage of common stock return for the 12 months ending 2 months before the block trade announcement minus the return on the CRSP equal-weighted index. Leverage is measured as the book value of long-term debt over the book value of assets. Insider holding variable is the percentage of shares owned by officers and directors and includes shares owned by individuals related to a member of the top management team, employee pension or stock option plans, trusts for which managers have some voting authority, and any other blocks of shares over which a member of the top management team has voting authority. Outsider-dominated board dummy is a dummy variable that takes the value of 1 when more than 60% of the board's directors are outsiders of the company. Top exec is founding family dummy variable is a dummy variable that takes the value of 1 when the top executive is a member of the founding family. Individual acquirer dummy is a dummy variable that takes the value of 1 when the acquirer is an individual. Dollar values are in millions.

  2. ***Significant at the 1% level.

  3. **Significant at the 5% level.

  4. *Significant at the 10% level.

Block premium (%)9.317.4217.8013.456.925.2610.88***
Firms with positive premium (%)69.6575.4468.023.52
Percent of shares acquired (%)12.489.3315.3713.7811.678.403.70**
Transaction value (mil)51.4012.7957.4615.8349.7011.077.76
Prior firm performance (%)7.725.87−3.961.3411.017.33−14.97*
Total asset (mil)758.1288.95713.4076.62770.7092.94−57.30
Leverage0.420.390.510.430.390.380.12
Insider holding (%)7.832.846.132.038.313.01−2.18
Institutional ownership (%)25.4920.2624.2819.8825.8320.40−1.56
Outsider-dominated board dummy0.8110.8510.8010.05
Board size7.4777.0177.607−0.59
Top exec is founding family dummy0.1800.1700.180−0.01
Individual acquirer dummy0.1600.1300.170−0.04
Sample size 738 162 576 

Following Barclay and Holderness (1989), the block premium is defined as follows.

  • image(1)

The block premium averages 9.31% for the whole sample. This figure is smaller than the average block premium of 16% reported by Barclay and Holderness (1989). The difference may arise from the difference in the sample periods: 1978–1982 in Barclay and Holderness and 1987–2005 in this study. Also, by using the Thomson SDC database, we are able to construct a sample of 738 block trades whereas Barclay and Holderness searched the Wall Street Journal to identify 63 block trades. Because the Wall Street Journal may report only newsworthy events, our sample includes block trades that are less dramatic.

For block trades that are followed by top executive turnover within one year of the block trade, the premium averages 17.80%. However, for trades that are not followed by top executive turnover within one year, the premium averages only 6.92%. The difference is significant at the 1% level. This difference in the block premium indicates that there may be benefits to having control over the firm, as indicated by the change in the top executive, over and above the benefits of just owning a block of shares of the firm.

Also, more shares of the company (15.37% vs. 11.67%) are acquired for block trades that result in subsequent top executive turnover. This implies that the acquirer is more likely to exercise control in the firm when he or she holds more shares of the firm.

Table 1 also reveals that, on average, insiders control 7.83% of the firm's shares in our sample. Insider ownership includes shares owned by individuals related to a member of the top management team, employee pension or stock option plans, trusts for which managers have some voting authority, and any other blocks of shares over which a member of the top management team has voting authority. By way of comparison, Morck, Shleifer, and Vishny (1988) report that the average ownership of all officers and directors is 10.6% in a sample of 371 Fortune 500 firms, and Mikkelson and Partch (1989) report an average insider ownership of 19.6% in a random sample of NYSE and AMEX firms. Our sample firms therefore have smaller insider ownership. This is consistent with Bethel, Liebeskind, and Opler (1998), who find that companies with high insider ownership are less likely to experience block share purchases. The low insider ownership of our sample implies that block trades of 5% or more shares of the company can confer a significant amount of controlling power to the new blockholder.

Consistent with previous studies (e.g., Mikkelson and Partch 1989; Denis, Denis, and Sarin 1997), firms that experience top executive turnover have worse performance. However, with respect to insider ownership, the fraction of firms with outsider-dominated boards, and the fraction of firms where the top executive is a member of the founding family, we do not find significant differences between firms with top executive turnover and firms without top executive turnover within one year of the block trade.

III. Results for Private Benefits

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References

Determinants of Block Premium

Table 2 shows the results of a cross-sectional regression of block premiums using the following model:

  • image(2)
Table 2. Determinants of Block Premium.
Category of Independent VariablesIndependent VariablesDependent Variable: Block Premium (%)
Coefficientp-value
  1. Note: This table shows the results of a cross-sectional regression of block premiums. Block premium (%) is defined as 100 ×{(price per share paid for the block) − (exchange price one day after the announcement of the transaction)}/(exchange price one day after the announcement of the transaction). Percentage of shares acquired is the percentage of the firm's equity that is acquired in the block transaction. Top executive turnover dummy is a dummy variable that takes the value of 1 if the block-traded firm experiences a top executive turnover within one year of the block trade. Prior firm performance is the percentage of common stock return for the 12 months ending 2 months before the block trade announcement minus the return on the CRSP equal-weighted index. Log of firm size is the natural logarithm of the book value of assets. Leverage is the book value of long-term debt over the book value of assets. Tangibility of assets is fixed assets over total assets. Acquirer is in the same industry dummy is a dummy variable that takes a value of 1 when the acquiring company is in the same industry group as the target, based on the two-digit Standard Industrial Classification (SIC) code. Bank acquirer dummy is a dummy variable that takes the value of 1 when the acquirer is a financial company. Major industry group dummies are based on the two-digit SIC code.

  2. **Significant at the 5% level.

  3. *Significant at the 10% level.

Ownership levelPercentage of shares acquired (%)0.08**.01
Control activityTop executive turnover dummy7.86**.04
Characteristics of the blockPrior firm performance (%)0.003.11
Log of firm size (mil)−0.73.76
Leverage0.41.17
Tangibility of assets (%)−0.13*.07
Characteristics of the acquirerIndividual acquirer dummy−2.80.22
Acquirer is in the same industry dummy0.65.15
Bank acquirer dummy−2.86.30
Industry dummiesIndustry–agriculture, forestry, fishing−1.91.51
Industry–mining−2.50.68
Industry–construction0.43.33
Industry–transportation and utilities2.63.60
Industry–wholesale trade−1.23.25
Industry–retail trade3.10.10
Industry–finance, insurance, real est.1.67.60
Industry–services−2.02.23
R2    0.34

Possible factors that can affect the premium can be categorized into the ownership level of the company, the relation between the block trade and the control activities within the firm, and control variables that capture characteristics of the block-traded company and of the acquirer.

A larger fractional ownership gives the blockholder more power in terms of having greater voting rights. Larger ownership also provides greater protection from a hostile takeover or proxy contest. Beyond a certain point, however, few additional private benefits will result from increased fractional ownership if the blockholder holds a sufficient amount of shares. There are also costs to owning a large portion of shares, such as monitoring costs or the costs that ensue from a possible lack of diversification in the owner's portfolio. This means that blockholders may not want to accumulate shares beyond a certain threshold. If the threshold varies by firm, there may be no discernible relation between fractional ownership and block premium. Barclay and Holderness (1989) and Dyck and Zingales (2004) show a positive relation between block premium and the size of shares acquired. The results in Table 2 confirm that the block premium tends to be greater as the fractional size of the block increases.

To examine how the size of private benefits is related to actual control activity as manifested though top executive replacement, we examine cases of top executive turnover within one year of the block trade.6 In the regression, we include a dummy variable that takes a value of 1 for block trades that are followed by a top executive turnover within one year of the block trade. The results show that block premiums are indeed larger for trades that are followed by change in the top executive. The expected block premium goes up by 7.86% for block trades that are followed by top executive turnover in the firm within one year of the block trade. Assuming that block premiums for trades that are followed by top executive turnover reflect private benefits of having control of the company in addition to private benefits of ownership, the results suggest that there are private benefits from having control of the firm (as reflected by the coefficient of the top executive turnover variable) over and above the benefits from having just ownership of the firm (as reflected by the coefficient of the percentage of shares acquired variable).

We use the following control variables to capture the characteristics of the block-traded company. First, the size of private benefits will be greater if the firm is performing well and smaller if the firm is having financial difficulties (Barclay and Holderness 1989). We measure the prior year's market-adjusted stock return using the percentage of common stock return for the 12 months ending 2 months before the block trade announcement minus the return on the Center for Research in Security Prices (CRSP) equal-weighted index. Second, firm size may affect the block premium. On one hand, block premium may increase with firm size because larger firms offer potentially greater benefits, both pecuniary and nonpecuniary (Barclay and Holderness 1989). On the other hand, the costs of being a blockholder may also increase with firm size, as larger firms are more likely to be monitored closely by security analysts, government officials, and institutional investors. We measure firm size as the natural logarithm of the firm's book value of total assets. Third, the size of debt may affect the block premium. However, a priori, a relation between private benefits and debt is not clear. Debt can have a negative effect on private benefits by constraining access to free cash flow (Jensen 1986). In contrast, debt can also increase one's effective control over corporate assets (Harris and Raviv 1988; Stulz 1988), thereby increasing the size of private benefits. Fourth, private benefits may also be related to the tangibility of assets because acquirers of the block can face more difficulty in diverting resources if assets are tied down and easily observable. Finally, private benefits may also differ across industries. Demsetz and Lehn (1985) suggest that owners of companies in the media, entertainment, and sports industries enjoy greater private benefits. We capture industry differences by categorizing companies whose blocks are traded into their major industry groups based on the two-digit Standard Industrial Classification (SIC) code. Manufacturing (where the two digits of the SIC code lie in the range of 20–39) is the most common industry group and is the excluded category in our regression.

We also use the following control variables that capture the characteristics of the acquirer. First, we include a dummy variable for acquisition by individuals because individuals, compared to corporate blockholders, have the added benefit of consuming perquisites (Demsetz and Lehn 1985). Second, where the acquirer is a corporation rather than an individual, there may be more private benefits for the acquirer to enjoy if the acquiring company is in the same industry as the target company. Thus, we include a dummy variable that takes a value of 1 when the acquiring company is in the same industry group as the target company based on the two-digit SIC code. Finally, if the acquirer is a financial company, it may acquire shares mainly for financial reasons and not for the consumption of private benefits. Thus, we include a dummy variable that takes the value of 1 when the acquirer is a financial company.

The results in Table 2 show that the block premium is marginally larger for firms that have better prior performance. As for the tangibility of assets, whereas Dyck and Zingales (2004) find an insignificant relation between block premiums and the tangibility of assets in their international study, we find a negative and significant relation (at the 10% level) between block premiums and the tangibility of assets. As for different industries of the block-traded companies, only the retail trade industry group dummy turns out to be a significant factor of block premiums.7

Determinants of Top Executive Turnover

A possible shortcoming of the approach used in the previous section is the endogeneity of top executive turnover. Although we do not know the blockholder's true intention with regard to exercising control when he or she acquires a block of shares, we can examine variables that can serve as ex ante proxies for the likelihood of top executive turnover. The likelihood of top executive turnover is different for each block trade because circumstances surrounding the block trade are unique for each block trade. These circumstances are a function of various factors, such as prior firm performance, ownership structure, and board characteristics, which have been shown in previous studies to affect the likelihood of top executive turnover. Certain circumstances surrounding a block trade may indicate greater likelihood of top executive turnover than for other block trades. Under situations where the current top executive position is at risk at the time of the block trade, the acquirer may be willing to pay more for a block of shares in anticipation of exercising control in the company.

Thus, in this section, we estimate the implied probability of the blockholders' exercising control, as manifested by the likelihood of top executive turnover at the time of the block trade. Later, we measure how the block premium changes with respect to this implied probability of top executive turnover.

We estimate logit regressions relating the probability of top executive turnover to firm performance, ownership characteristics, and board composition. Model 1 is the basic model and model 2 includes interaction terms between prior firm performance and ownership structure/board composition variables that are shown in the previous literature to influence the likelihood of management turnover.

  • image(3)

The explanations for some of the key independent variables are as follows. First, we include the percentage of shares acquired in the block trade because a blockholder who owns a greater proportion of a firm's shares has more voting power in the company and has more incentive to work toward value-increasing activities such as replacing incompetent CEOs. Second, we include the insider ownership variable because more managerial shareholdings may better align the interests of managers and shareholders, and provide managers with a greater incentive to invest in value-increasing activities (Jensen and Meckling 1976; DeAngelo and DeAngelo 1985). However, greater inside shareholdings can also entrench management by making it more difficult to transfer control and remove a manager (Stulz 1988; Mikkelson and Partch 1989; Denis, Denis, and Sarin 1997). Third, we include the outsider-dominated board dummy because studies suggest that internal monitoring is improved by having a higher fraction of outside directors (Weisbach 1988; Byrd and Hickman 1991; Brickley, Coles, and Terry 1994). Following the classification used by Denis, Denis, and Sarin (1997) and Weisbach (1988), we categorize a board as outsider dominated if at least 60% of the company's board members are outsiders. Finally, we include the founding family dummy because according to Morck, Shleifer, and Vishny (1988), the manager's status as the founder of the firm may be conducive to managerial entrenchment, and Denis, Denis, and Sarin empirically show that the top executive is less likely to be replaced if he or she is a member of the founding family.

The results of logit regressions are provided in Table 3. Numbers in the table refer to marginal effects where derivatives are evaluated at mean values of the variables. The marginal effects reported in Table 3 will change accordingly as the values of some independent variables change in our subsequent analyses. The results in Table 3 are mostly consistent with previous empirical studies on top executive turnover. The likelihood of top executive turnover is positively related to the block size and negatively related to prior firm performance, insider ownership, and the company-founder dummy. We also find that an outsider-dominated board alone does not affect the likelihood of top management turnover, which is consistent with the results of Weisbach (1988) and Denis, Denis, and Sarin (1997).

Table 3. Determinants of Top Executive Turnover.
Category of Independent VariablesIndependent VariablesDependent Variable: Top Executive Turnover
Model 1Model 2
  1. Note: This table shows the estimates of logit models relating the probability of top executive turnover. Numbers are marginal effects where derivatives are evaluated at mean values. The dependent variable is the top executive turnover dummy variable, which takes the value of 1 if the block-traded firm experiences a top executive turnover within one year of the block trade. Percentage of shares acquired is the percentage of the firm's equity that is acquired in the block transaction. Prior firm performance is the percentage of common stock return for the 12 months ending 2 months before the block trade announcement minus the return on the CRSP equal-weighted index. Insider ownership variable is the percentage of shares owned by officers and directors and includes shares owned by individuals related to a member of the top management team, employee pension or stock option plans, trusts for which managers have some voting authority, and any other blocks of shares over which a member of the top management team has voting authority. Outsider-dominated board is a dummy variable that takes a value of 1 when more than 60% of the board's directors are outsiders. Top exec is founding family dummy variable is a dummy variable that takes the value of 1 when the top executive is a member of the founding family. Firm size is the natural logarithm of the book value of assets. The coefficients of interaction terms are based on the cross-partial derivative of the expected value of the dependent variable. Dollar values are in millions. The p-values are in parentheses.

  2. **Significant at the 5% level.

  3. *Significant at the 10% level.

Block sizePercentage of shares acquired (%)0.051**0.049**
 (.04)(.04)
Firm performancePrior firm performance (%) (RET)−0.010*−0.011*
 (.08)(.09)
Ownership structureInsider ownership (%)−0.011** 
 (.04) 
Dummy for 5% < (insider ownership) < 25% −0.219*
  (.09)
Dummy for (insider ownership) > 25% −0.545**
  (.03)
Board compositionOutsider-dominated board dummy−0.154−0.073
 (.64)(.56)
Status of top executiveTop exec is founding family dummy−0.233*−0.206*
 (.07)(.08)
Firm sizeLog of firm size (mil)−0.032*−0.034*
 (.07)(.07)
Interaction termsRET * [Dummy for 5% < (insider ownership) < 25%] 0.009**
  (.04)
RET * [Dummy for (insider ownership) > 25%] −0.004
  (.18)
RET * [Outsider-dominated board dummy] 0.006**
  (.03)
Pseudo R2 0.1180.129

In model 2 of Table 3, we consider the possible effects of insider ownership and the existence of outsider-dominated boards on the sensitivity of top executive turnover to performance. Following Ai and Norton (2003) and Powers (2005), the coefficients of interaction terms and the tests for statistical significance are based on the cross-partial derivative of the expected value of the dependent variable after allowing for the nonlinearity of the model. Denis, Denis, and Sarin (1997) find a weaker relation between performance and turnover in firms with high insider ownership. Also, the existence of an outsider-dominated board is found to have a significant influence on the sensitivity of turnover to performance by Weisbach (1988), whereas it is shown to be insignificant by Denis, Denis, and Sarin. We follow Denis, Denis, and Sarin, and Morck, Shleifer, and Vishny (1998) and classify firms into three categories of managerial ownership: at most 5%, between 5% and 25%, and greater than 25%.

The results of model 2 in Table 3 show that firms with insider ownership that exceeds 25% have 55% less probability of top management turnover. The results also show that insider ownership has a significant effect on the sensitivity of turnover to performance. The probability of turnover is negatively related to performance when insider ownership is less than 5%. However, the positive coefficient (0.009) on the interaction of the prior performance variable with the dummy variable denoting an insider ownership between 5% and 25% indicates that the probability of turnover is significantly less sensitive to performance for firms in this ownership structure category. In fact, for firms with insider ownership between 5% and 25%, the likelihood of top executive turnover is almost unaffected by the past performance of the company (−0.011 + 0.009 =−0.002). This is consistent with the finding of Denis, Denis, and Sarin (1997).

Surprisingly, we find that firms with outsider-dominated boards manifest less sensitivity of top management turnover to performance. This result is contrary to that of Weisbach (1988), who finds a stronger association between prior performance and the probability of top executive turnover for companies with outsider-dominated boards. The difference may occur because our sample firms are confined to companies whose blocks are traded. During times of possible control contest, an existing insider of the company may not continue to be aligned with the incumbent managers. Insiders may push toward replacing the top executive when opportunities arise (in this case, the entry of a new blockholder coupled with poor firm performance) for gaining control of the company or being a part of the team that gains control. Thus, for block-traded companies, it is possible to observe greater sensitivity of top management turnover to performance for companies with insider-dominated boards, which is equivalent to observing less sensitivity of top management turnover to performance for companies with outsider-dominated boards.

Two-Stage Regression

After computing the likelihood of top executive turnover at the time of the block trade, we now measure how the block premium changes with respect to this implied probability of top executive turnover. We run a two-stage least squares model where the first-stage equation explains the endogenous top executive turnover variable and the second-stage equation explains the block premium as the dependent variable.

The basic model is stated below:

First-stage equation:

  • image

Second-stage equation:

  • image(4)

The results for the two-stage regression of block premium are shown in Table 4. This is the main regression in this study. Model 1, the basic model, includes the probability of top executive turnover variable (T/O1) from model 1 of Table 3 as an explanatory variable. Model 2 includes the probability of top executive variable (T/O2) from model 2 of Table 3 as an explanatory variable.

Table 4. Two-Stage Regression of the Block Premium.
Category of Independent VariablesIndependent VariablesDependent Variable: Block Premium (%)
Model 1Model 2Model 3Model 4Model 5
  1. Note: This table shows the results of a recursive regression model for estimating the block premium, where the probability of top executive turnover is treated as endogenous. Probability of top executive turnover is the implied probability of top executive turnover at the time of the block trade and is created from a logit regression of Table 3. This probability is then multiplied by 100. Probability of executive turnover_1 is the implied probability of turnover from model 1 in Table 3. Probability of executive turnover_2 is the implied probability of turnover from model 2 in Table 3. All other variables are defined as in Table 2. Major industry group dummies based on the two-digit Standard Industrial Classification (SIC) code and year dummies are included in the regression (not reported). Dollar values are in millions. The p-values are in parentheses.

  2. **Significant at the 5% level.

  3. *Significant at the 10% level.

Ownership levelPercentage of shares acquired (%) (Block)0.129**0.140**0.096**0.098**0.118**
 (.04)(.04)(.03)(.04)(.03)
Control level[Prob of top executive turnover_1] (%) (T/O1)0.092** 0.073**  
 (.01) (.02)  
[Prob of top executive turnover_2] (%) (T/O2) 0.095** 0.041**0.022**
  (.02) (.02)(.02)
Characteristics of the blockPrior firm performance (%)0.0010.0020.0010.0030.002
 (.13)(.14)(.11)(.12)(.14)
Log of firm size (mil)0.019−0.0390.018−0.015−0.008
 (.66)(.69)(.78)(.75)(.67)
Leverage0.2920.2680.317*0.3250.319*
 (.10)(.11)(.09)(.10)(.09)
Tangibility of assets (%)−0.119*−0.103**−0.098**−0.113**−0.102**
 (.06)(.04)(.04)(.03)(.03)
Characteristics of the acquirerIndividual acquirer dummy2.325*2.658*1.881*1.7901.884*
 (.09)(.10)(.09)(.11)(.09)
Acquirer is in the same industry dummy0.6520.6840.5270.4030.483
 (.13)(.17)(.11)(.15)(.11)
Bank acquirer dummy−4.371−4.964−4.688−4.580−4.371
 (.29)(.26)(.27)(.24)(.26)
Square and interaction termsBlock * T/O1  0.012* 0.011**
   (.08) (.04)
Block * T/O2   0.018** 
    (.03) 
[Prob of top executive turnover_2]2    0.002**
     (.04)
[Percentage of shares acquired]2    0.009
     (.46)
Adjusted R2 0.270.250.280.300.33

The results in Table 4 show that both the percentage of shares acquired and the probability of top management turnover significantly affect the block premium. The positive coefficient of the percentage of shares acquired implies there are private benefits from having ownership of the firm. According to model 1, owning 20% more shares of the firm increases the block premium by approximately 2.6%. The positive coefficient of the probability of top executive turnover variable indicates there are private benefits from having control of the company over and above private benefits that arise from just owning the company. In a hypothetical case of going from having no likelihood of top executive turnover within one year of the block trade to having 100% chance of top executive turnover within one year of the trade, the block premium jumps by more than 9%.

The relation between prior firm performance and block premium becomes statistically insignificant in the two-stage regression model. In our model, prior firm performance influences the block premium in two ways. The first is an indirect way through the likelihood of top executive turnover variable. Better performing firms are less likely to replace their top executive as shown in the first-stage equation in (4), the results of which are shown in Table 3. This lower likelihood of top executive turnover will result in a smaller block premium in the second-stage equation in (4). Second, in a direct way, better performing firms are associated with larger block premiums because blockholders anticipate enjoying more private benefits from better performing firms after controlling for the likelihood of top executive turnover. Therefore, we find that prior firm performance, after explaining the likelihood of top executive turnover in the first-stage equation, has a statistically insignificant influence on block premium in the second-stage equation.

The coefficient of the tangibility of assets variable is significantly negative, as in Table 2. Therefore, blockholders seem to anticipate having more private benefits from companies that have a higher proportion of intangible assets. The results for other control variables are mostly similar to those in Table 2. The difference from Table 2 is the coefficient of the individual acquirer dummy variable. In Table 2, we find that individual acquirers are associated with smaller, albeit not statistically significant, block premiums. The result for the better specified model in Table 4 shows that the coefficient of the individual acquirer dummy is now positive and significant at the 10% level. This is consistent with Holderness and Sheehan (1988), who find that block premiums are larger for individuals than they are for corporations. This also supports the argument of Demsetz and Lehn (1985) that individuals, compared to corporate blockholders, have the added benefit of being able to consume perquisites.

In models 3 and 4, we include the interaction term between the percentage of shares acquired and the probability of top executive turnover. Model 3 uses the probability of top executive turnover as defined by model 1 of Table 3, and model 4 uses the probability of top executive turnover as defined by model 2 of Table 3. The interaction term between the percentage of shares acquired and the probability of top executive turnover is significant at the 10% level in model 3 and at the 5% level in model 4. This suggests that private benefits from having ownership and control reinforce each other. The results for other variables are similar to those in models 1 and 2.

In model 5, we include square terms for the probability of top executive turnover and the percentage of shares acquired. The square term of the probability of top executive turnover is statistically significant at the 5% level whereas the square term of the percentage of shares acquired is not significant. Thus, the block premium increases at an increasing rate with respect to the implied probability of top executive turnover. However, block premium does not increase at an increasing rate with respect to the fraction of shares acquired. This suggests that private benefits increase at an increasing rate as the new blockholder is more likely to exercise control over the firm, but increase only at a constant rate as the blockholder's ownership level rises.

Figure I captures the essence of this study. It shows a three-dimensional plot depicting the relation among the block premiums, block size, and probability of top executive turnover. On the basis of model 5 of Table 4, we calculate the expected values of the block premium for different values of the block size and the probability of top executive turnover within one year of the block transaction. Except for the block size variable, all right-hand-side variables in the first-stage regressions of equation (4) that explain the probability of executive turnover are chosen so that they lie within the same standard deviation from their respective means. For all other variables in the second-stage regression of equation (4) for estimating the block premium given a certain block size and probability of executive turnover, we use their mean values.

As can be seen in Figure I, private benefits, as measured by the block premium, increase slowly with respect to the ownership level, as measured by the percentage of shares acquired, and increase rapidly with respect to the likelihood of exercising control, as measured by the probability of top executive turnover within one year of the block trade.

We show numerical examples in Table 5.8 For a 10% block trade, the expected block premium is: 1.20% if there is a 0% likelihood of subsequent top executive turnover, 5.75% if there is a 25% likelihood of subsequent top executive turnover, and 12.80% if there is a 55% likelihood of subsequent top executive turnover. However, for a 25% block trade, the expected block premium is: 2.38% if there is a 0% likelihood of subsequent top executive turnover, 9.68% if there is a 25% likelihood of subsequent top executive turnover, and 19.48% if there is a 55% likelihood of subsequent top executive turnover. Thus, the block premium increases at an increasing rate with respect to the probability of top executive turnover, holding all else constant.

Table 5. Average Percentage Block Premiums for Different Block Sizes and Probabilities of Top Executive Turnover.
Probability of Top Executive TurnoverBlock Size as a Percentage of Firm's Equity
5101520253050
  1. Note: In this table, we calculate block premiums according to model 5 of Table 4 given different values of the block size (percentage of shares acquired) and the implied probability of top executive turnover. Right-hand-side variables (except for the block size) in the first-stage regression of equation (4) for explaining the probability of executive turnover are chosen so that they lie within the same standard deviation from their respective means. For all other variables in the second-stage regression of equation (4) for estimating the block premium, given a certain block size and probability of executive turnover, we use the mean values.

00.611.201.792.382.973.565.92
101.582.723.865.006.147.2811.84
202.954.646.338.029.7111.4018.16
253.795.757.729.6811.6513.6121.47
304.726.969.2011.4413.6815.9224.88
406.899.6812.4715.2618.0520.8432.00
509.4612.8016.1419.4822.8226.1639.52
7517.6422.3527.0731.7836.5041.2160.07
10028.3134.4040.4946.5852.6758.7683.12

However, with respect to the percentage of shares acquired, the block premium increases at a constant rate, holding all else constant. For example, for block trades that have a 25% probability of top executive turnover, the expected block premium is: 5.75% for a block trade with a block size of 10%, 9.68% for a block size of 25%, and 13.61% for a block size of 30%.

Table 5 also gives us a measure of nonpecuniary private benefits. For blockholders to enjoy pecuniary private benefits, they need to have some control over the firm's activities. For example, pecuniary benefits such as receiving excessive salary or tunneling through self-dealing transactions require exercising control in the company. On the other hand, blockholders can enjoy nonpecuniary private benefits even without exercising any control in the company. For example, the feeling of being a “proud owner” of a company; becoming part of the business network; interacting with influential and well-known businessmen, politicians, and celebrities; achieving higher social status; and enjoying the recognition, fame, and prestige that accompany the higher social status, can be achieved without exercising control. Therefore, the block premium when the likelihood of top executive turnover is zero (or very close to zero) can be used to estimate the magnitude of nonpecuniary private benefits. The results in Table 5 show that even when there is little chance of exercising control in the company, some investors are willing to pay a premium to be a blockholder. It is at this extreme where the blockholder has very little chance of exercising control in the company that we measure the nonpecuniary private benefits. In our sample, nonpecuniary private benefits range from 0.61% (for a block trade of 5% of the firm's shares) to 5.92% (for a block trade of 50% of the firm's shares) of the share price. The fraction of nonpecuniary benefits out of the total private benefits ranges from 18% (for a block trade of 5% of the firm's shares) to 29% (for a block trade of 50% of the firm's shares) when total private benefits are computed at the mean values of the variables. This shows us that although nonpecuniary private benefits are smaller in size than pecuniary private benefits, nonpecuniary private benefits constitute a nontrivial portion of the total private benefits.9

As one must be wealthy to purchase a block of shares, we think that becoming a large owner of a company is another way of pursuing the “lifestyles of the rich and famous” and that considerable nonpecuniary benefits must be embedded in the premium these blockholders pay to acquire blocks of shares. The reason blocks are traded at a premium even when there are only private benefits of ownership or nonpecuniary benefits may resemble why people pay an extraordinary price to acquire a piece of art or a wine collection.

Robustness Tests

Subsample of Positive Block Premiums Some blocks of shares are traded at a discount rather than at a premium. This is because the ownership of a block of shares not only brings benefits, but also costs, such as monitoring costs, inventory costs, and the costs of possibly carrying an undiversified portfolio. If these costs outweigh the benefits, block trades will occur at a discount. Therefore, it should be noted that the block premium is a net benefit measure of private benefits. However, to avoid the possible ambiguity of interpreting negative net private benefits, we examine whether our results hold for the subsample of block trades with positive block premiums. This subsample, as shown in Table 1, consists of 514 block trades, or 69.65% of our entire sample.

We repeat the same test procedures for the subsample of block trades that occur at a positive premium. The results reported in Panel A of Table 6 are similar to those in Table 5, which reports the results for the whole sample of block trades. Thus, our results are robust to whether block trades occur at a premium or a discount.

Table 6. Robustness Tests of Block Premium Regressions.
Panel A. Two-Stage Regression of the Block Premium for Block Trades with Positive Premiums
 
Category of independent variablesIndependent variablesDependent Variable: Block Premium (%)
Model 1Model 2
Ownership levelPercentage of shares acquired (%) (Block)0.132**0.145**
 (.05)(.03)
Control level[Prob of top executive turnover_1] (%) (T/O1)0.094*** 
 (.01) 
[Prob of top executive turnover_2] (%) (T/O2) 0.093**
  (.02)
Adjusted R2 0.310.29
Panel B. Alternative Specifications of Two-Stage Regression of the Block Premium
 
Category of Independent Variables Dependent Variable: Block Premium (%)
Model 1Model 2Model 3Model 4Model 5
  1. Note: Panel A shows the results of recursive regression models for estimating the block premium for a subsample of block trades with positive block premiums. Probability of executive turnover_1 is the implied probability of turnover from model 1 in Table 3. Probability of executive turnover_2 is the implied probability of turnover from model 2 in Table 3. The control variables used are the same as in Table 4 (not reported). Panel B shows the results of alternative specifications of control activities for the recursive regression models for estimating the block premium. In model 1, prob of board turnover is the implied probability of board turnover within one year of the block trade. Explanatory variables used for board turnover are prior firm performance, percentage of shares acquired, log of firm size, insider ownership, and outsider-dominated board dummy. In models 2 and 3, changes in capital expenditure and R&D expenditure are the implied changes in capital structure and R&D, respectively. Explanatory variables used are cash flow, Tobin's Q, and log of firm size. In models 4 and 5, changes in leverage and dividends are the implied changes in leverage and dividends/earnings, respectively. Explanatory variables used are cash flow, return on assets (ROA), and log of firm size. The control variables used in the second-stage regression (not reported) are the same as in Table 4 except for the exclusion of the leverage variable. The p-values are in parentheses.

  2. ***Significant at the 1% level.

  3. **Significant at the 5% level.

  4. *Significant at the 10% level.

Ownership levelPercentage of shares acquired (%)0.061**0.055**0.563**0.054**0.057**
 (.03)(.04)(.04)(.03)(.04)
Control levelProb of board turnover (%)0.002    
 (.23)    
Change in capital expenditure (%) 0.017**   
  (.06)   
Change in R&D expenditure (%)  0.001  
   (.42)  
Change in leverage (%)   0.002 
    (.12) 
Change in dividends (%)    0.000
     (.41)
Adjusted R2 0.080.170.060.090.11

Other Control Activities Although replacing the top executive is the most significant manifestation of a control activity, there can be other ways in which the blockholder can exert influence in the firm. Thus, besides examining only the changes in the top executive position, we check how expected changes in board members, investment policy (capital expenditure and R&D), and financial policy (leverage and dividend payout) affect the size of private benefits.

Panel B of Table 6 shows the results of alternative specifications for the recursive regression model of estimating the block premium. The effect of the likelihood of board turnover on the block premium is examined in model 1. Using a similar methodology that was used to explain top executive turnover, the probability of board turnover is the implied probability of board turnover at the time of the block trade and is created from the first-stage logit regression where the explanatory variables are prior firm performance, percentage of shares acquired, log of firm size, insider ownership, and outsider-dominated board dummy. The results show that the expected likelihood of board turnover is not a statistically significant factor of the block premium.

The effects of the expected changes in capital expenditure and R&D on the block premium are shown in models 2 and 3, respectively. The change in capital expenditure variable is the implied change in capital expenditure at the time of the block trade and is created from the first-stage logit regression where the explanatory variables, following Bertrand and Schoar (2003), are cash flow, Tobin's Q, and the log of firm size. The change in R&D variable is constructed in the same way. The results show that the expected change in capital expenditure positively affects the size of the block premium, whereas the expected change in R&D is not a significant determinant of the block premium. Under the situation where capital expenditure is expected to increase by 100%, the block premium increases by 1.7%. This implies that increased capital expenditure, such as investing in pet projects, may be one way through which private benefits are realized.

The effects of the expected changes in leverage and dividend payout ratio are shown in models 4 and 5, respectively. The change in leverage is the implied change in leverage at the time of the block trade and is created from the first-stage logit regression where the explanatory variables, following Bertrand and Schoar (2003), are cash flow, return on assets (ROA), and the log of firm size. The change in dividend payout ratio is constructed in the same way. The results show that both measures of financial policy are not significant determinants of the block premium.

To summarize, when we measure private benefits that result from additional control activities, the change in capital expenditure is the only variable that significantly affects the block premium. This suggests that capital expenditure may be one channel where the block owner can extract private benefits from the company. However, the likelihood of increasing capital expenditure seems to have much smaller economic significance than the likelihood of top executive turnover. A 50% increase in the expected capital expenditure increases the block premium by only 0.85%, whereas a 50% increase in the likelihood of top executive turnover increases the block premium by 4.6%.

IV. Conclusion

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References

Nonpecuniary private benefits noted in studies such as Jensen and Meckling (1976), Demsetz and Lehn (1985), and Barclay and Holderness (1989) are inherently difficult to measure. In this study, we provide an estimate of the size of nonpecuniary private benefits by using the block premium that is associated with block trades. We first decompose private benefits into benefits that accrue from having ownership of the firm and benefits that accrue from having control of the firm. This decomposition is possible because each block trade, which results in partial ownership and partial control of the company, is different with respect to both the level of ownership and the level of control that it brings to the new blockholder. The decomposition allows us to quantify the amount of pecuniary and nonpecuniary private benefits. Although much of private benefits arise from having control over the firm, private benefits that do not involve.

Appendix

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References

Appendix: Private Benefits and Shared Benefits

If the new blockholder engages in value-increasing activities in the company following the acquisition of the block as noted by Barclay and Holderness (1989) and Bethel, Liebeskind, and Opler (1998), there will be shared benefits to be enjoyed by all shareholders. These shared benefits will be reflected in the ex post stock price after the announcement of the block trade. Therefore, block trades provide us with an opportunity to analyze not only private benefits but also the accompanying shared benefits. Whereas the size of private benefits has been estimated in many studies, the corresponding size of shared benefits has not been examined. In this Appendix, we examine the size of shared benefits and analyze how our decomposition of private benefits into ownership component and the control component affects the relative size of shared benefits to private benefits.

In Table A1, we estimate the size of shared benefits by examining the public's reaction to block trade announcements. Specifically, we measure CARs for the block-traded companies surrounding the date of the block trade announcement. Using a preestimation window of 250 days, ending 21 days before the announcement of the block trade, we find CARs using a market model (Panel A) and a Fama–French three-factor model (Panel B). For both models, we find positive and significant abnormal stock returns surrounding the announcements of block trades. Shareholders realize a 7.6% to 9.3% abnormal return for 1 to 3 days surrounding the announcement of a block trade and more than 14% for a longer period starting 20 days before the block trade. Block trades, therefore, bring out shared benefits to all shareholders in addition to private benefits that accrue solely to the blockholder. And considering the average block premium of 9.31%, results suggest that there are sizable shared benefits compared to private benefits. Table A1 shows that block trades that are followed by top executive turnover produce greater shared benefits on average than do block trades that are not followed by top executive turnover.

Table A1. Shared Benefits: Cumulative Abnormal Returns (CARs) Surrounding Block Trades.
 Whole SampleSubsequent CEO TurnoverNo Subsequent CEO TurnoverDifference in Mean
  1. Note: In this table, we report changes in stock prices surrounding the announcements of block trades. Mean CARs are calculated using a market model in Panel A and using a Fama–French three-factor model in Panel B. Day 0 is the announcement date of a block trade. Each CAR is measured using a preestimation window of 250 days, ending 21 days before the announcement of the block trade.

  2. **Significant at the 5% level.

  3. *Significant at the 10% level.

Panel A. CARs Using the Market Model
 
CAR (0, +1) 7.62%10.52% 6.80%3.72%**
CAR (–1, +1) 8.98%11.84% 8.18%3.66%**
CAR (–2, +1) 9.25%11.37% 8.65%2.72%*
CAR (–20, +1)14.12%17.60%13.14%4.46%
CAR (–20, +20)14.51%19.10%13.22%5.88%
Number of observations738162576 
 
Panel B. CARs Using the Fama–French Three-Factor Model
 
CAR (0, +1) 7.59%10.57% 6.75%3.82%**
CAR (–1, +1) 9.00%11.95% 8.17%3.78%**
CAR (–2, +1) 9.26%11.58% 8.61%2.97%*
CAR (–20, +1)14.03%16.84%13.24%3.60%
CAR (–20, +20)14.22%18.95%12.89%6.06%*
Number of observations738162576 

To examine the determinants of the relative size of shared benefits to private benefits, we estimate a recursive regression for the ratio of shared benefits to private benefits while treating the likelihood of top executive turnover as endogenous. This set of regressions is expressed in equation (A1):

First-stage equation:

  • image

Second-stage equation:

  • image((A1))

The first-stage equation finds the expected likelihood of top executive turnover variable at the time of the block trade and is retrieved from model 1 of equation (3). The second equation estimates the ratio of shared benefits to private benefits. CARs are calculated by the market model. To avoid having zero or a negative number in the numerator in the ratio of CAR to block premium, we use a subsample of block trades with positive block premium. This subsample consists of 514 block trades, or 69.95% of our entire sample. The explanatory variables used for the second equation are the same as those used to explain private benefits.

Results in Table A2 show a negative relation between CEO turnover probability and the ratio of shared benefits to private benefits. Although the size of shared benefits was greater for block trades that involve CEO turnover in Table A1, the relative size of shared benefits to private benefits is less when there is greater likelihood of CEO turnover. This suggests that while the expectation of the blockholder's control activities during times of greater likelihood of CEO turnover can benefit both shareholders and the blockholder, cases where the blockholder is not expected to engage in control activities during times of lower likelihood of CEO turnover result in less private benefits to be enjoyed by the blockholder, hence bringing relatively more shared benefits than private benefits. In the latter case where the blockholder does not engage in control activities, there must be other roles of the blockholder, such as simple monitoring activities, that the public seems to value.

Table A2. Two-Stage Regression of the Relative Size of Shared Benefits to Private Benefits.
Category of Independent VariablesIndependent VariablesDependent Variable: [(CAR)/(Block Premium)] (%)
CAR (0,+1)CAR (–1,+1)CAR (–2,+1)CAR (–20,+1)CAR (–20,+20)
  1. Note: This table shows the results of a recursive regression model of estimating the ratio of shared benefits to private benefits while treating the probability of top executive turnover as endogenous and using a subsample of block trades with positive block premium. Shared benefits are measured using mean cumulative abnormal returns (CARs) measured using a market model, with a preestimation window of 250 days, ending 21 days before the announcement of the block trade, where day 0 is the announcement date of a block trade. Private benefits are measured using block premium. The dependent variable is 100 × (CAR)/(block premium). Probability of top executive turnover is the implied probability (%) of management turnover at the time of the block trade and is created from a logit regression of model 1 in Table 3. Two-digit Standard Industrial Classification (SIC) dummies are included in the regression. All other variables are explained in Table 4. The p-values are in parentheses.

  2. **Significant at the 5% level.

  3. *Significant at the 10% level.

Ownership levelPercentage of shares acquired (%)−0.024−0.040−0.017−0.033−0.007
 (.16)(.18)(.19)(.24)(.33)
Control level[Prob of top executive turnover] (%)−0.518*−0.533*−0.538−1.042−1.109
 (.08)(.07)(.12)(.18)(.23)
Characteristics of the blockPrior firm performance (%)−0.013−0.015−0.030−0.048−0.053
 (.21)(.27)(.28)(.32)(.31)
Log of firm size (mil)0.0710.0530.0470.0050.012
 (.78)(.76)(.74)(.63)(.69)
Leverage0.0160.0410.0300.045-0.091
 (.45)(.48)(.42)(.51)(.54)
Tangibility of assets (%)0.0430.040*0.0390.0510.078
 (.14)(.10)(.12)(.15)(.13)
Characteristics of the acquirerIndividual acquirer dummy−6.438−8.243−6.910−9.826−8.312
 (.32)(.39)(.40)(.54)(.58)
Acquirer is in the same industry dummy4.314**4.746**5.403**8.491**11.208*
 (.03)(.02)(.02)(.04)(.07)
Bank acquirer dummy3.29*3.51*3.87*5.406.06
 (.08)(.07)(.08)(.13)(.19)
Adjusted R2 0.130.120.110.080.05

With respect to the block size variable, we show in Table 4 that as the block size increases, the blockholder expects to have more private benefits. In Table A2, the insignificant coefficient of the block size shows that as the ownership level increases, shared benefits are increasing along with private benefits.

The ratio of shared benefits to private benefits is higher when the acquirer of the block is in the same industry as the company whose block is traded. This result, along with the result that this variable was insignificant in explaining the size of private benefits in Table 4, suggests that although the potential synergy in acquiring company in the same industry does not lead to increase in private benefits, it does lead to more shared benefits. The result for the bank acquirer dummy shows that the size of shared benefits relative to private benefits is greater when financial institutions acquire block of shares. This result is consistent with Bethel, Liebeskind, and Opler (1998), who find that firms targeted by financial investors are more likely to be underperforming firms where performance improvements are possible, even though these firms are less likely to experience extensive operational changes.

To summarize, from examining the increase in share price surrounding the announcement of the block trade, we find that small shareholders get as many benefits in the form of stock price increase as blockholders expect to get in the form of private benefits. The relative size of shared benefits to private benefits is greater when the likelihood of top executive turnover is low, suggesting that there are non-control-related value-increasing activities of the blockholder.

Footnotes
  • 1

    Barclay and Holderness (1989) measure the premium by using the postannouncement price as a benchmark because the price that follows the block trade announcement will reflect the shared benefits (represented by the cash flow rights) of the block trade. On the other hand, the privately negotiated block trade price will reflect both private and shared benefits. Thus, the difference between the block trade price and the postannouncement exchange price will reflect only private benefits. The analysis on the relation between private benefits and shared benefits is provided in the Appendix.

  • 2

    It is likely that greater ownership will accompany greater control of the company. However, as long as the ownership level and control level are not perfectly correlated, it is possible to decompose private benefits into ownership and control. The example concerning persons A and B in the preceding paragraph illustrates this point.

  • 3

    Holderness and Sheehan (1988) find that many corporate majority shareholders place their representatives in top management positions.

  • 4

    We would like to thank the referee for pointing out this limitation of the estimate.

  • 5

    The exact size of the bias is α(1 −λ)(Yb − Ys), where α∈[0,1] is the fractional size of the block, λ∈[0,1] is the bargaining power of the shareholder who is selling the block, and Yb (Ys) is the security value of the buyer (seller).

  • 6

    As shown in Table 1, we find that 22% of block trades are followed by the top executive turnover within one year of the trade. Holderness and Sheehan (1988) show that for majority block trades, 71% of the trades involve turnovers among the three top managers within one year of the trade. The difference in the turnover ratios between the two studies seems to stem from the fact that our sample consists of trades that involve partial control of the firm, whereas block trades in Holderness and Sheehan transfer full control.

  • 7

    When we instead assign a dummy variable for firms in the media, entertainment, and sports industries, whose first two digits of the SIC code are 27, 48, 78, or 79, or the first three digits are 731, the dummy variable remains insignificant.

  • 8

    The numbers in Table 5 are out-of-sample estimates of block premiums for a given block size and probability of executive turnover. Even when the block size is less than 50% of the firm's shares, other variables can cause the probability of executive turnover to be 100%, as can be seen from the first-stage regression of equation (4) and Table 3.

  • 9

    One may argue that blockholders have both pecuniary and nonpecuniary private benefits when they have control of the company. In this case, our measure of nonpecuniary private benefits when the control level is zero may represent a lower bound of the true size of nonpecuniary private benefits. This strengthens our finding that nonpecuniary private benefits comprise a sizable amount relative to total private benefits.

References

  1. Top of page
  2. Abstract
  3. I. Introduction
  4. II. Data
  5. III. Results for Private Benefits
  6. IV. Conclusion
  7. Appendix
  8. References