This manuscript has improved greatly as a result of comments from an anonymous referee and Jerry Zimmerman (the editor). I am grateful for the comments and guidance provided by my dissertation committee, Rachel Hayes, Thomas Lys, Edward Zajac, and especially Robert Magee (chair). Helpful comments were received from Ron Dye, Tom Fields, Ole-Kristian Hope, Ramu Thiagarajan, Ross Watts, and workshop participants at the University of British Columbia, Chinese University of Hong Kong, Hong Kong University of Science and Technology, INSEAD, London Business School, Northwestern University, University of Oregon, University of Rochester, and Tulane University. Financial support was received from the Kellogg Graduate School of Management, the Institute of Chartered Accountants of Alberta, and the Social Sciences and Humanities Research Council. Data used in this study may be obtained from the author upon request.

Economic consequences of regulated changes in disclosure: the case of executive compensation

Kin Lo

Faculty of Commerce and Business Administration The University of British Columbia

2053 Main Mall, Vancouver, BC, Canada, V6T 1Z2 Ph. (604) 822-8430 Fax (604) 822-9470

Email: kin.lo@commerce.ubc.ca

July 2002

Abstract The 1992 revision of executive compensation disclosure rules in the U.S. could have benefited shareholders by inducing corporate governance improvements or harmed them by increasing disclosure costs. Consistent with the governance improvement hypothesis, companies that lobbied against the regulation had, relative to control firms: (i) return-on-assets and return-on-equity that improved by 0.5% and 3%, respectively; and (ii) excess stock returns of 6% over the 8-month period between the announcement and the adoption of the proposed regulation. Also, firms lobbying more vigorously against the proposal had more positive abnormal stock returns during events that increased the probability of regulation. Key Words: Disclosure, Executive Compensation, Securities Regulation JEL Classification: D61, G38, K22, M40

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1. Introduction

One central theme in accounting research is the economic consequence of changes in reporting

rules.1 The documented effects validate the theory that stakeholders are affected by regulated

changes in financial reports because contracts rely on those reported numbers. Prior research

examines regulations that affect the recognition of values on accounting statements, such as net

income. In contrast, this study considers a change in regulation that alters disclosures only:

executive compensation disclosures provided to shareholders in the annual proxy statement.2

The Securities and Exchange Commission (SEC) in 1992 adopted regulations aimed at

increasing the quality and quantity of compensation disclosures. The SECs goal (as stated by

the Director of the SECs Division of Corporate Finance, for example) was to encourage

improved corporate governance by increasing the transparency of the amounts and ways in

which executives are remunerated (Quinn, 1995). Thus, the SEC expected the new rules to

increase shareholder value. From a contrary perspective, if the extra disclosures were beneficial

to firms, that information would have been provided without regulation; increasing the regulated

minimum level of disclosure would impose additional costs on companies.

This study takes three complementary approaches to examine whether the change in SEC

rules was beneficial to investors. The first approach uses stock returns surrounding the key

events to gauge the change in shareholder wealth. The second method analyzes the operating

performance of firms that lobbied the SEC in the years surrounding 1992 to determine whether

the new rules affected managers motivation to maximize shareholder value. The third approach

1 Fields, Lys, and Vincent (2001) provide reviews of related studies. 2 McGahran (1988) found that new perquisite disclosures were associated with a shift in pay from perquisites to monetary compensation, but the effect is due to the inclusion of the perquisite in the employees taxable income, that is, the employees financial statements. Wysocki (1999) examines the informativeness of SFAS 131 segment disclosures, which does not directly affect recognition, but he does not examine their economic consequences.

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examines firms lobbying activity in the form of comment letters to the SEC, as a reflection of

corporate executives assessment of the regulations effect on them personally. Overall, the

results are more consistent with the SECs view that additional mandated disclosures enhance

shareholder wealth. However, whether costs to other parties exceed these benefits remains an

open question.

2. Hypothesis Development

Since 1938, the Securities and Exchange Act of 1934 has required registrants to publicly disclose

information on their executives compensation. The revision to these rules in 1992 was the

SECs response to demands for better information on executive compensation.3 On February 13,

1992, Richard Breeden, (then) Chairman of the SEC, announced that more stringent disclosure

rules were on the way. The announcement outlined the major components of the new rules,

including a summary table that includes practically all forms of compensation, a comparison of

pay and stock performance, and an explanation of incentive compensation by the compensation

committee (Salwen, 1992). Following the release of the detailed proposal on June 23, 1992, both

investors and corporate representatives lobbied the SEC aggressively.4 The Commission adopted

the final rules on October 16, 1992, for implementation five days later. While the final rules

were not identical to the proposal, the major elements of the proposal were adopted. In

particular, all 10 items in the proposal, such as the summary compensation table and

compensation committee report, were adopted with only minor revisions.

The president of the United Shareholders Association, Ralph Whitworth, hailed the new

rules as sweeping reforms [that] pave the way for shareholders to take back their companies

3 Johnson (1995, 20) notes, The difficulty in correctly ascertaining an executives actual compensation from all sources had been a common complaint 4 In the executive summary of the final rules, the SEC notes it had received over 900 comment letters.

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(Johnson, 1996, 196). This statement reflects the popular belief that poor governance of

executive compensation was widespread, with low quality disclosures contributing to that poor

governance. However, the rules were proposed and adopted at a time of unprecedented political

pressure and could have been the SECs response to that pressure.5 This view suggests that the

rules could have negative effects on shareholders by inducing changes to compensation contracts

that were previously optimal. These two arguments are developed more fully below.

2.1 Governance Improvement Hypothesis

The concern expressed by the public and investor groups over executive pay indicates that there

was a widespread belief that contracts with managers are sub-optimal. The fact that individual

investors and investor groups lobbied aggressively for the adoption of more extensive

compensation disclosures (see footnote 4) suggests that, in their estimation, such disclosures

would improve corporate governance.

The improvement in compensation contracts could be obtained by reducing four sources

of friction between shareholders and managers. First, there are agency costs associated with the

indirect contracting between shareholders and management through the board of directors.

While boards have a fiduciary duty to shareholders, agency theory suggests that a priori boards

will not contract as shareholders themselves would because their incentives differ. Jensen (1993)

argues that boards ineffective discipline of management is a prime contributor to the failure of

internal control systems. While the effects of stock ownership, legal liability, and reputation do

help align the interests of boards with those of shareholders, these are only mitigating factors.

They reduce but do not eliminate the shareholder-board incentive differences, just as such forces

5 Signs of such political pressure include Senate hearings on Runaway Executive Pay in May 1991, the Corporate Pay Responsibility Act (H.R. 2522 and S. 1198, not adopted) in June 1991, and Section 162(m) of the Internal Revenue Code limiting the deductibility of compensation enacted in 1993.

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do not by themselves solve the prototypical moral hazard problem between owners and

managers. The requirement for the compensation committee to prepare a report describing the

major performance measures used to reward management could further reduce this incentive

difference by encouraging committee members to more diligently participate in the contracting

process.6 In addition, results from organizational behavior research predict that board members

will behave differently if their actions are public instead of private (e.g., Diekmann, 1997).

Second, there could be asymmetry in the information available to the board and the

manager. It is reasonable to assume that executives have incentives to be informed for purposes

of negotiating their own compensation contracts. They are likely to access compensation

consultants, possibly at no personal cost. In contrast, even though the board could also use

consultants, it has less incentive to do so than the manager since the personal benefits to board

members are much lower. The requirement for a compensation committee report encourages the

compensation committee to reduce the information asymmetry in order to justify compensation

policies to shareholders. For example, the committee is more likely to engage compensation

consultants to become more informed. While it remains possible for compensation committees

to provide boilerplate descriptions in their reports, a cursory examination of recent compensation

committee reports show that they are not just standardized, uninformative statements.

Third, there could be collusion between the compensation committee and management.

Specifically, collusion could result from interlocking relationships between members of the

committee and management. The requirement to disclose interlocking relationships should

discourage such collusion.

6 As discussed in textbooks on managerial accounting, the benefits of budgeting derive partly from the process (rather than the budget itself) by stimulating communication and analysis of the business (Zimmerman 2003, Chapter 6). Similarly, the requirement for a compensation committee report may encourage more careful consideration of executive pay packages, even if the report itself were not particularly informative.

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Fourth, low quality information dissemination to shareholders hinders their ability to

monitor both the board and the manager. In particular, if ownership is diffuse, each shareholder

will find insufficient benefit to offset the high costs of understanding a convoluted proxy

statement. The increased information and standardization make it less costly to collect and to

process the information. Consequently, shareholders are more likely to complain if

compensation practices appear sub-optimal.

The argument that disclosure could mitigate contracting frictions and increase firm value

raises the question of why firms did not voluntarily disclose more. That is, if additional

disclosures signal better incentive contracting, firms could disclose more to distinguish

themselves from inferior types to increase their stock prices. However, several facts show that

firms are reluctant to disclose compensation information. First, a detailed review of 25 randomly

chosen proxy statements issued in 1991 shows that companies did not provide information

beyond that mandated by the SEC. Second, all corporate submissions to the SEC were in favor

of the 1983 relaxation of compensation disclosure rules. In addition, firms rarely disclose any

compensation information in jurisdictions where such disclosures are not required. For example,

Canadian companies generally did not disclose compensation information prior to regulation by

the Ontario Securities Commission in 1993.

There are two reasons why firms do not voluntary disclose compensation beyond that

required by regulation. First, disclosing more may entail additional costs to the firm or the

executives who determine the disclosure policy. If all firms disclosed the same amount of

compensation information, and one firm tries to distinguish itself by disclosing more, that

company may attract the attention of compensation critics or outspoken shareholders. Even

though the companys compensation structure may in fact be superior to those in other

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companies, the firm may wish to avoid the political costs associated with additional scrutiny.

Along the same lines, compensation disclosure can be viewed as a collective good, because the

ability to compare different companies enhances the value of those disclosures. However, if

individual firms do not find it in their interests to disclose, a prisoners dilemma equilibrium

would result and no firms would provide the disclosure (Leftwich, 1980).

Second, if the increase in firm value associated with more disclosure does not translate

into higher compensation for managers, they would have no incentive to disclose more. While

equity-based compensation does increase with firm value, the additional disclosure may bring

about changes in the contracts that reduce the managers (utility from) total compensation. If

managers are extracting rents from the existing compensation contract at the expense of

shareholders, they will have no reason to change the status quo to reduce those rents.

The differential implications for managers under these two explanations result in opposite

lobbying positions: in the prisoners dilemma explanation, managers would lobby in support of

the regulation, while they would lobby against increased disclosure in the second scenario. The

data show an overwhelming opposition to the regulation by managers (see section 3.3)

suggesting that the latter explanation is more plausible.

If the governance improvement arguments were valid, we would expect several

consequences from the adoption of more stringent compensation disclosure rules. First, there

would be abnormal stock returns surrounding events that increase the probability of adoption,

particularly for firms in which the executives resisted the new regulation, as investors bid up

stock prices in anticipation of the governance improvements. Similarly, better governance would

lead to compensation contracts that better align the interest of managers and shareholders.

Increased managerial effort would translate into improvements in operating performance (e.g.,

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return on book assets and equity). Third, executives who expect to see the most improvement in

governance in their companies and the biggest reductions in rents from their compensation

contracts would be the ones who most vigorously object to the rules. Formally, the first

hypothesis and predictions (in alternative form) are as follows:

H1 GOVERNANCE IMPROVEMENT: The expansion of compensation disclosure rules resulted in, or were anticipated to lead to, value-increasing governance improvements. P1A Stock returns between samples: In the period surrounding events that increased the regulations probability of adoption, firms that lobbied against the new compensation disclosure regulation tended to have higher stock returns than those that did not lobby. P1B - Operating performance: Firms that lobbied against the new compensation disclosure regulation tended to have returns on assets and equity that were abnormally low before the regulation change and their performance improved subsequent to the regulation. P1C - Stock returns within sample of lobbyers: In the period surrounding events that increased the regulations probability of adoption, within the set of lobbying firms, those who lobbied most vigorously tended to have higher stock returns.

Note that the hypothesized positive stock price reaction and operating performance changes

derive primarily from improved contracting leading to better alignment of manager and

shareholder interests. The effect of any reduction in compensation expense is secondary in

magnitude, because even 100% of executive pay is only a small proportion of firm value.

2.2 Disclosure Cost Hypothesis

There are several potential sources of costs related to compensation disclosure. One source is

suggested by Jensen and Murphy (1990), who conclude that political costs prevent higher pay-

performance sensitivities. If the new rules allow less compensation to remain unreported,

companies could be induced to adversely alter their contracts to mitigate additional political

costs from reporting higher compensation.

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Firm values could also be reduced if managers and compensation committees become

more concerned about the appearance of the compensation contract rather than about its

efficiency in motivating value-maximizing actions and sharing risk. If managers and

compensation committees want to reduce the amount of shareholder and public scrutiny of

executive pay, they might contract so that pay correlates closely with current year stock returns,

for example, even though those contracts may impose inefficient levels of risk on the manager.

As Sloan (1993) argues, accounting performance measures are used in compensation contracts to

shield executives from market-wide fluctuations. In addition, it may be optimal for a manager to

be (partly) compensated based on operating results that are anticipated in the future due to

actions taken in the current year (Hayes and Schaefer, 2000). While such contracts may be

optimal, boards concerned about investor pressure may not adopt them because current pay

appears not to be closely related to performance.

Value reduction could also result from the release of proprietary information. The

voluntary disclosure literature suggests that firms will voluntarily disclose information as long as

the information increases shareholder value. A necessary condition for a full disclosure

equilibrium is the absence of disclosure costs (Grossman 1981; and Milgrom 1981), in which

case, minimum disclosure regulations are redundant. A partial disclosure equilibrium results if

there are proprietary or nonproprietary costs of disclosure (Verrecchia 1983, Wagenhofer 1990).7

In this case, imposing a minimum disclosure level that is above the endogenously determined

amount would reduce shareholder wealth. In particular, the firm may be required to disclose

proprietary information it would not otherwise reveal, or it could alter its compensation contract

in ways that would prevent that proprietary information from being transmitted through

7 Partial disclosure equilibriums arising from uncertainty of whether managers have private information (Dye 1985) is not descriptive here, as shareholders do know that managers do possess compensation information.

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compensation and its disclosure. In either case, the actions reduce shareholder wealth. As an

example, disclosing information showing high executive compensation may lead to higher labor

costs if there is an increased probability that unions will be able to negotiate higher wages

(Jensen and Murphy 1990).

A broader view of the disclosure literature suggests another reason that the disclosure

regulation might impose costs on firms. It is conceivable that firms with better compensation

policies would choose to disclose more in order to distinguish themselves from other firms, so

that a separating equilibrium results. The fact that this equilibrium was not observed before the

regulatory change suggests that the costs of disclosure outweighed the benefits of doing so.

In summary, the disclosure cost hypothesis argues that compensation contracts are

already optimal without regulation so expanded mandatory disclosures are possibly detrimental

to the contracting parties. This reasoning leads to Hypothesis 2, again with three predictions:

H2 DISCLOSURE COST HYPOTHESIS: The expansion of compensation disclosure rules resulted in, or were anticipated to lead to, value-decreasing changes in contracts. P2A Stock returns between samples: In the period surrounding events that increased the regulations probability of adoption, firms that lobbied against the new compensation disclosure regulation tended to have lower stock returns than those that did not lobby. P2B - Operating performance: Firms that lobbied against the new compensation disclosure regulation tended to have returns on assets and equity that were not systematically high or low before the regulation change, and their performance deteriorated subsequent to the regulation. P2C - Stock returns within sample of lobbyers: In the period surrounding events that increased the regulations probability of adoption, within the set of lobbying firms, those who lobbied most vigorously tended to have lower stock returns.

Tests of this disclosure cost hypothesis and the governance improvement hypothesis are

described below in Section 4. Before proceeding, the sample is described next.

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3. Sample Selection and Descriptive Statistics

Two samples are used in this study: a sample of firms that lobbied the SEC and a control sample

of firms that did not. While free riding is a concern in voting and lobbying settings, the lobbyers

should be, on average, those affected most by the regulation. To the extent that the control

sample includes firms that are substantially affected, but that did not lobby, such

misclassification would tend to reduce the power of the tests to reject the null hypothesis of zero

differences between samples, but should not produce significant results when there are none.

The procedures used to collect these samples are described in Section 3.1, followed by sample

statistics in Section 3.2. Sections 3.3 and 3.4 describe firms lobbying activity.

3.1 Sample Selection

The sample of lobbyers was identified by reviewing the letters commenting on the proposal,

which are in the SEC archives (File S7-16-92). Two hundred and ten firms submitted comments

to the SEC in the period between the proposal date and the close of the comment period, August

31, 1992. In cases where a company submitted more than one letter, the combined information

from all letters for the same firm are considered to be one observation. In addition, bar

associations, accounting firms, the Business Roundtable, the American Society of Corporate

Secretaries, and other similar organizations also submitted letters.8 For firms that made

reference to comments submitted by such organizations, those comments are counted as if the

firm made those comments directly. Of the 210 firms in the comment letter sample, 15 have

missing or insufficient return data in the files of the Center for Research in Security Prices

(CRSP). The remaining sample of 195 firms is used in analyses not requiring a control sample.

For purposes of testing whether the comment letter sample experienced abnormal stock

8 Incidentally, only half of the Big 6 accounting firms submitted comments on behalf of their clients.

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returns and operating performance, a matching control sample is required. Recent studies

examine the efficacy of various matching procedures. Barber and Lyon (1996) examine

measures of operating performance such as return on assets and recommend performance-based

matching. However, they also conclude that matching by industry and firm size yield well-

specified and powerful test statistics (360, 396). Kothari and Warner (1997) and Barber and

Lyon (1997) examine the measurement of long-term (>1 year) stock returns. The latter paper

concludes, matching sample firms to control firms of similar size and book-to-market ratios

yield well-specified test statistics (370). Combining these findings, it would be best to construct

a control sample based on industry, size, and book-to-market. However, a reasonable match

along all three dimensions is not feasible. Consequently, this study uses a control sample

matched on industry and size, while book-to-market is included as a control variable in

regressions involving long-term stock returns. Industry is defined using SIC codes and size

using market values of equity (MV). The details of the matching algorithm are as follows:

1. For each comment letter firm i, identify the SIC code. 2. Calculate the size distance (Distance(i,j)) between firm i and every non-

lobbyer j with the same four-digit SIC. Distance(i, j) is defined as |ln(MV(i)) ln(MV(j))| = |ln(MV(i) / MV(j))|. Such a definition is insensitive to the ordering of the comparison.

3. Select the j that minimizes Distance(i,j). Denote this firm as j*. 4. If Distance(i,j*) ln(4), then firm j* is selected as a match and removed from

the list of potential match firms. 5. If Distance(i,j*) > ln(4), then no matching firm is identified. 6. Within each SIC group, the algorithm begins with the smallest firm so that the

best overall match obtains in terms of proximity on a dollar basis. 7. Steps 2 to 5 are repeated at the three- then two-digit SIC levels for the

remaining unmatched comment letter firms.9 Of the 195 firms with stock return data, five could not be matched; so 190 firms are used

9 An alternate matching algorithm could minimize the difference in dollar values. However, this alternative is less reliable since, for a hypothetical $10 billion firm, a $1 billion firm would be judged to be a closer match than one worth $20 billion. Common sense suggests that the latter firm is a better match since it is only twice as large as the test firm, whereas the test firm is 10 times as large as the $1 billion firm.

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in analyses using the matched-pair design. The 190 control firms are distinct, as sampling is

performed without replacement. Seventy percent of the matches (133 firms) were at the four-

digit SIC level, while 11 and 19 percent (21 and 36 firms) were at the three- and two-digit levels.

Panel A of Table 1 summarizes the results of the sample selection procedures.

3.2 Financial Statistics

Panel B of Table 1 contains the descriptive statistics of the comment letter and control samples.

The statistics show that the matching procedure was successful at matching firm sizes the

median market values of equity do not differ significantly ($2.57 vs. $2.16 billion, Wilcoxon p =

0.21).10 However, other indicators of size show some differences: the comment letter samples

median book values of equity and assets are significantly higher. The $6.95 billion mean equity

market value for the 190 comment letter firms equals a combined market value of $1.32 trillion,

or 38% of the total of all firms in the CRSP database. The control sample comprises another

23%, so the samples in this study cover a majority of the population by market value.

The significant differences in the book value of equity also result in significant

differences in the book-to-market ratio (median 0.61 vs. 0.53). Since Fama and French (1992)

find that book-to-market is an important factor explaining expected returns, returns analysis over

longer horizons need to take this difference into account.

While the firms that submitted comments have insignificantly different after-tax return on

assets on average (median of 3.62% vs. 3.98%, p = 0.10), they have a significantly lower return

on equity (median of 11.15% vs. 13.07%, p = 0.04). Comparisons of the means for these

statistics are consistent with those for the medians.

10 The median is the correct measure of the success of the matching procedure because the distribution of differences is predictably right skewed, since the matching algorithm minimizes the difference in the log of equity market values. In other words, the algorithm tries to find two firms that yield a ratio of market values that is closest to

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3.3 Lobbying Activity

From the comment letters, it is possible to determine the companys position regarding the

SECs proposed rules. The letters generally have a negative tenor, with 160 of 195 firms

explicitly opposing the rules in their entirety. A further 17 firms do not provide an overall

opinion. The remaining 18 firms state support for the proposal. The indicator variable Oppose

takes on a value of one for the 160 firms that clearly stated opposition, and zero for the other 35

firms.11 The low proportion of firms supporting the proposal (9%) suggests that there was an

overwhelming consensus among managers regarding the negative impact of the regulation, and

that the lack of voluntary disclosure is not a result of a prisoners dilemma. Given the small

number of observations in that category, the 18 supporting firms are not analyzed as a separate

sample below, aside from being coded zero in the variable Oppose.12

Objections to the proposal of a more specific nature are classified into eight categories.

Eight (0, 1) indicators identify whether a company voiced a particular objection.

Object1 = 1 if a firm asked the SEC to provide less onerous provisions for smaller firms.

Object2 = 1 if a firm stated that the compliance costs would be too high.

Object3 = 1 if a firm objected to the inclusion of a report from the compensation committee.

Object4 = 1 if a firm said that a dollar value should not be attached to stock option grants.13

Object5 = 1 if a firm disagreed with the disclosure of future stock option values based on hypothetical rates of appreciation in stock prices.

Object6 = 1 if a firm did not want a graph depicting the performance of the company compared to a market index and a peer group of companies.

unity, so a ratio of 2/1 is deemed as close a match as a ratio of 1/2. 11 The neutral and supporting firms have been combined in subsequent analyses because of (i) the small number of observations in each category, and (ii) preliminary comparisons of the stock returns and operating performance show no significant difference between the two sub-groups. 12 Results in Table 5 through Table 8 are not sensitive to excluding the supporting firms. 13 These comments are intriguing since the proposal explicitly states that there will be no requirement to value stock options at the grant date. It seems the issue was so important to these firms that they felt compelled to make it known that option valuation was not acceptable, whether it is for executive stock options or for broader plans.

14

Object7 = 1 if a firm disagreed with the disclosure of options with lowered strike prices.

Object8 = 1 if a firm objected to the disclosure of interlocks in the compensation committee.

These objections were submitted not just by firms explicitly opposed to the proposal, but also by

the 35 firms who were apparently supportive or neutral.

3.4 Statistics on Lobbying Activity

Table 2 contains information pertaining to the content of the comment letters. The main

diagonal in Panel A tabulates the eight specific objections, and the overall stance of the

company. The most common objection was Object3 on the inclusion of compensation

committee reports, with 146 comments whereas the least frequent was Object1 on relief for small

firms, with nine comments. In addition, Panel A shows the number of instances in which each of

two types of objections is observed together. For instance, of the 160 letters opposed to the new

disclosure rules overall, 130 also objected to the inclusion of compensation committee reports.

Hence, conditional on a firm having Oppose = 1, the probability of Object3 is 130/160 = 0.81.

Panel B shows these conditional probabilities. These probabilities show that the comment

variables overlap to a high degree, particularly objections 2, 3, 5, and 6.

Given the high degree of dependence of the various objections, the specificity of the

objections may be artificial. It is possible that firms were not really disagreeing with anything in

particular, but wanted to raise any issue possible to support the position that the proposal should

be rejected in its entirety. Also, from a purely econometric viewpoint, the collinearity of these

variables makes inferences difficult if they are used as explanatory variables at the same time.

To address these issues, factor analysis is relied upon to reduce the number of independent

variables. The input for the analysis are seven variables, Object2 through Object8; Object1 is

not included because the nine firms submitting comments relating to small firm size a priori

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have very different motivations than the remainder. (Unreported factor analysis shows that

excluding Object1 is indeed warranted.) The estimation is carried out by maximum likelihood.

As Panel A of Table 3 shows, there is one and only one common factor for the seven variables.

This common factor substitutes for the seven objection variables in some of the analyses below.

Another parsimonious way to aggregate the various objections is to compute the sum of

the objections voiced by each company. The variable Nobject is defined as the sum of the seven

indicator variables, Object2 to Object8. As shown in Panel B of Table 3, the variable Nobject is

97% correlated with the common factor. This high correlation suggests that there should be little

difference in substituting Nobject for the common factor.

4. Results

Three sets of results are presented below, which correspond to the three predictions of the

hypotheses. Section 4.1 analyzes the two samples average stock returns and Section 4.2

compares their average operating performance. The cross-sectional relation between stock

returns and lobbying behavior within the lobbying sample are examined in Sections 4.3 and 4.4.

4.1 Stock Returns

Prior research has focused on key events leading up to the adoption of regulations because such

events are hypothesized to have the largest impact on the likelihood of adoption (Leftwich, 1981

and Dechow et al., 1996). This study looks at the following three events in 1992, which were

previously described in Section 2:

Event 1 the announcement by Chairman Breeden on February 13;

Event 2 the release of the proposed rules on June 23; and

Event 3 the adoption of the final rules on October16.

These events are crucial points in the regulatory process and are thus likely to significantly affect

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investors assessment of whether the SEC will adopt the new regulations. To the extent that

investors had anticipated these events, or news was disseminated outside these three event dates,

the power of the returns tests will be reduced.

Two other events were considered but excluded from analysis because they were judged

to have little potential to affect investors expectations. This determination was made without

looking at returns so as to avoid data-snooping biases. One of these events is a speech by the

SECs Director of Corporate Finance on November 8, 1991. The speech transcript provides no

specifics on the future regulation. Given the overall political environment at the time, such

vague comments are unlikely to have swayed investors. The other event is a speech by an SEC

Commissioner on February 20, 1992, which essentially reiterated the points made by the SEC

Chairman one week earlier in Event 1.

The short windows used to capture the abnormal returns are the three days (-1, 0, +1)

centered on each of the three dates identified above, consistent with most event studies.14 A

period of 200 days before Event 1 is used to estimate the market model parameters, using the

CRSP value-weighted index as the market portfolio.15 Abnormal returns are calculated as the

prediction errors from this market model. However, as noted in Section 3.2, the test sample

comprises 38% of the market portfolio, so returns in the test sample will non-trivially affect the

market portfolio. As a result, the market model will tend to understate the magnitude of the

prediction errors, reducing the power of the tests. To mitigate this problem, I also examine raw

returns, which do not adjust for systematic movements in the market.16

14 Sensitivity analyses show that inferences are unchanged with two-day event windows. 15 No substantive differences result if the estimation period for each event is the 200 days before that event. The chosen approach avoids the contamination of the estimation period with prior event days. 16 To see how the market model understates the magnitude of abnormal returns in this study, assume a beta of 1 and that the regulation has a hypothetical impact on test firms of 1%, and no other information is released in the market. The market return would be increased by 0.38%, so that the average market model adjusted return is 1 0. 38 =

17

To address the possibility that a substantial portion or even most of the news relating to

the regulation was released outside these three event dates, I also examine longer term abnormal

returns spanning the period between day 1 of Event 1 and day +1 of Event 3. While this

analysis should capture a much higher portion of any impact of the regulation, it is expected to

have low power since the variance of long-term returns will be high. In addition, the statistical

significance of findings becomes more susceptible to misspecification as the horizon increases

(Barber and Lyon, 1997), so caution is warranted when interpreting the results of this analysis.

While Barber and Lyon suggest using buy-and-hold returns, reported results use cumulative

abnormal returns to be consistent with short-window tests; the effect of using buy-and-hold

returns does not alter inferences.17

With longer accumulation periods, factors affecting expected returns become more

important. To account for the size and book-to-market factors (Fama and French, 1992), I

estimate the following equation for the 380 firms in the two samples:

iiiiCommenti edBMcSizebDaCAR ++++= , , (1)

where CAR is abnormal return accumulated from Event 1 -1 day to Event 2 +1 day (or Event 3

+1 day), DComment is an indicator equaling 1 if the firm is in the comment sample, Size is the log

of market value, and BM is the book-to-market ratio. In this equation, the coefficient on DComment

is the difference in returns between the samples, after controlling for size and book-to-market.

It is important to note that while this return analysis uses information from the comment

letters, which are not available until after Event 2, the maintained hypothesis is that investors are

able to correctly conjecture, on average, who will or will not lobby based on existing information

0.62%. Also note that other common measures of abnormal returns have the same or even more severe problems. For example, if size-adjusted returns are used, the predominance of comment letter firms in the largest decile results in the decile return being largely determined by the test firms. 17 Buy-and-hold returns are the product of firms returns, whereas cumulative abnormal returns use the sum.

18

regarding the companies, their compensation policies, governance, and disclosure policies.

Thus, lobbying activity and investor expectations should be correlated with each other.

Results

Table 4 shows the short-window market-model-excess and raw returns for the two samples. The

statistics generally show that there are no significant unusual price movements in the event

period. While the comment letter sample has significantly positive raw returns of 1.66%, mainly

due to the three days of the final rules adoption (t = 5.95), the control sample showed similar

positive returns, so that the difference between samples is insignificant (t = -0.02). Thus, short-

window stock return results provide support for neither H1 nor H2. Unreported results show that

inferences are unaffected when bootstrapped p-values or median returns are used.

It appears, however, that the stock market reaction to the regulation is substantially

dispersed over time. Figure 1 and Table 5 show that there are significant between-sample

differences in market-model-excess returns over the eight-month period of the regulations

deliberation. The return difference primarily occurs in the period between Event 1 and Event 2,

with a mean cumulative abnormal return of 5.82% with a significant t-statistic of 2.68.

(Unreported results using the median show similar results.) These cumulative returns increased

modestly to 7.23% by Event 3. Adding controls for firm size and book-to-market in Model 2

reduces these returns by roughly 1% but the differences remain significant.18

Note that the above analysis of returns uses the between-sample differences in market-

model excess returns. As a result, it controls for the market component of returns, as well as

returns that are related to industry and size. To gain further assurance that the excess returns are

not due to omitted risk factors, Figure 1 also plots the difference in cumulative excess returns

18 The 1% decline in abnormal returns when book-to-market is added to the regression is close to the magnitudes reported in Table IV of Fama and French (1992).

19

over two other 8-month periods, one ending just before Event 1, and one beginning just after

Event 3. The absence of any discernible patterns in the returns in these surrounding periods

provides additional confidence that the excess returns in the test period is not spurious.

Unreported results also show that these return differences are not a result of differences in

earnings surprises in the period. Nevertheless, one can only have limited confidence in

attributing long-term returns to any particular event. Taken in isolation, the abnormally positive

8-month returns provide only weak evidence in support of H1. However, other findings below

corroborate this result.

4.2 Operating Performance

The analysis of operating performance uses two measures common in the literature: return on

assets and return on equity. Both metrics are used since there is no consensus as to whether pre-

or post-leverage performance is more appropriate. However, the two metrics should yield

consistent inferences given the similarity in the leverage ratios of the two samples (see Table 1).

After-tax return on assets (ROA) is defined as income before extraordinary items

(Compustat item 18) plus after-tax interest (item 15 (1 - tax rate)) divided by the average of

assets (item 6) at the beginning and end of the fiscal year. Interest expense is added back

because ROA measures performance independently of leverage. The tax rate is calculated as

income taxes (item 16) divided by pretax income (item 170). Return on equity (ROE) is income

before extraordinary items (item 18) divided by average equity (item 60). Firms with negative

equity are excluded (five per year on average) since ROE is undefined for such observations.

ROA and ROE are known to have unusual distributional characteristics because small

denominators (possibly due to conservative accounting) can yield very large return numbers that

unduly influence the statistics, especially the mean. To mitigate this problem, values of |ROA| or

20

|ROE| exceeding one are winsorized to 1. In addition, to ensure that the results are not driven

by observations with small denominators, the aggregate ROA and ROE is computed for each

sample. Aggregate ROA (ROE) is obtained by dividing the sum of the sample firms before-

(after-) interest profits by the sum of their assets (equity), as previously defined. While these

aggregate measures avoid the small denominator problem, no test of statistical significance is

possible given that there is only one observation per sample. Nevertheless, the magnitudes of

the differences in operating performance provide indications of economic significance.

Results

Table 6 shows the results of these analyses, which covers the six-year period starting two years

before the event year (1992). The three panels in the table show the ROA and ROE means,

medians, and aggregate statistics, respectively. Using any or all of 1990, 1991, 1992 as

benchmarks, lobbying firms operating performance improved in later years, while there are no

noticeable changes for control firms. For example, Panel B shows that between 1990 and 1995,

the median ROA (ROE) increases by 0.39% (1.75%) for firms in the comment letter sample,

while control firms have a comparatively small decrease of 0.13% (0.66%), for a difference

between samples of 0.52% (2.41%). The change in operating performance is significantly

different between samples (ROA: Wilcoxon Z = 2.63 and ROE: Z = 2.87). The means show

similar changes in performance; however, the standard errors are higher due to the

aforementioned small denominator problem, so statistical significance is lower. On an aggregate

basis, the difference is similar, at about 0.56% for ROA and 3.38% for ROE.

To show that the comparisons of the first and last years are representative of the general

pattern over the six-year period, Figure 2 plots the annual between-sample differences in

operating performance contained in Table 6. Figure 2a plots the mean and median ROA and

21

ROE differences while Figure 2b plots the difference in the aggregate ROA and ROE. Both

charts show that the improvement between the first and last year is not isolated to those two

years. In summary, the evidence on operating performance is consistent with the Governance

Improvement Hypothesis and inconsistent with the Disclosure Cost Hypothesis 19

To better gauge the economic significance of these results, note from Table 1 that at the

end of 1991, lobbying firms combined book assets total $3,088 billion and book equity total

$626 billion. The difference in aggregate performance thus amounts to $17 to 21 billion of

income per year. These estimates of the economic impact of the regulation is in line with the

long-window returns discussed in Section 4.1. Specifically, capitalizing the $17 to 21 billion in

perpetuity at discount rates in the range of 12 20% (the range of historical equity returns)

results in $85 to 175 billion in value added. In comparison, the mean abnormal return of 5 to 7%

from Table 5 applied to the comment samples $1.32 trillion of equity market value (see Table 1)

equals $66 to 92 billion. Thus, notwithstanding the substantial estimation errors in accounting

and stock returns, both measures produce estimates that are of similar magnitudes.

These amounts are clearly economically significant in either percentage or monetary

terms. They may seem even implausibly large considering that the change in regulation affected

only disclosure. However, the governance improvement hypothesis argues that compensation

disclosure is a critical factor for motivating management. Results from prior research show that

(i) effective managers comprise a substantial portion of firm value (Hayes and Schafer, 1999)

and (ii) compensation plays an important role in motivating them (Jensen and Murphy, 1990;

Lys and Vincent, 1995). First, the large sums paid to CEOs and other top-level managers is

prima facie evidence of the crucial role they play in creating value for shareholders. More

19 In long-horizon analyses of stock or book rates of return, survivorship bias is potentially a confounding factor. To check whether survivorship could drive the results in this section, the analysis was repeated using samples that had

22

specific evidence in Hayes and Schafer suggests that differences in CEO talent are substantive:

when that talent is hired away, the average negative abnormal return is 1.5%. Note that this

amount represents just the difference in the value of the incumbent and the next best alternative

manager, so the total value of managerial talent as a percent of firm value is much higher.

Second, in addition to theoretical predictions that compensation is a primary determinant of

managerial decisions, evidence in Lys and Vincent shows that in the case of AT&Ts acquisition

of NCR, private monetary incentives for managers with a magnitude of around only $100,000,

nevertheless contributed to the pursuit of pooling-of-interest accounting that cost AT&T

shareholders upwards of $500 million. Thus, it is plausible that improved governance over

compensation could have such large economic consequences.

4.3 Cross-sectional Analysis - Association of Stock Returns and Lobbying Activity

This section analyzes the relation between returns and more specific lobbying behavior. The

rationale for these analyses is that one reasonably conjectures that executives who anticipate the

most harm would tend to lobby more vigorously. According to the Governance Improvement

Hypothesis, more lobbying indicates that the rules are anticipated to be more costly to the

manager, but more beneficial for governance and shareholders. On the other hand, if the

Disclosure Cost Hypothesis were true, then more vigorous lobbying would indicate that the

regulations are anticipated to be more harmful to both the executive and the firm.

This cross-sectional analysis is more powerful than the analysis of average returns in

Section 4.1, to the extent that a reliable measure of the degree of objection to the regulation can

be constructed. It is possible that certain managers (especially those in large firms where

research staff is available) may submit comments to the SEC as a matter of course, without fully

complete data through the six-year period. The results are very similar to those reported above.

23

considering the ramifications of the regulation. The 40 objections to option valuation is

consistent with this possibility, since the issue was explicitly excluded from the proposal. Thus,

the submission of a comment letter could be a noisy indicator of executives preferences. A

more detailed analysis of the content of the comment letters could possibly better identify those

who are genuinely affected by the rules.

The first regression model implemented relates abnormal stock returns with the eight

categories of objections, Object1 to Object8, and the overall position of the company, Oppose:

Model 1: iij

iji eOpposeajObjectaaAR +++= =

9

8

10 )( (2)

where ARi is the cumulative abnormal return for firm i on the nine days of the three events. As

noted earlier, the variable Common factor from factor analysis sufficiently captures the common

variance in Object2 to Object8, so Model 2 replaces the seven variables with the common factor.

Model 3 uses Nobject in place of Common factor as it is so highly correlated with the common

factor. Formally, these models in equation form are as follows:

Model 2: iiiii eOpposebfactorCommonbObject1bbAR ++++= 3210 . (3)

Model 3: iiiii eOpposecNobjectcObject1ccAR ++++= 3210 . (4)

Table 7 shows the predicted signs for the coefficients under each of the two hypotheses.

For studies of regulatory change such as this one, event returns are aligned in calendar

time. As a result, cross-sectional correlation of returns creates potentially serious inference

problems (Bernard, 1987). Not accounting for positive correlations will tend to overstate the

significance of the results. Two approaches are used to address this issue. First, the regressions

are also estimated using the method suggested by Sefcik and Thompson (1986), which accounts

for cross-sectional correlation in the errors. These results do not alter the inference qualitatively,

so the simpler OLS results are presented. Second, bootstrapped p-values have been calculated

24

and are presented along with the asymptotic OLS values. Specifically, bootstrapped p-values are

calculated as follows: one-tailed p-values are the proportions of 10,000 repetitions that generated

coefficients greater than the OLS coefficients in the table (less than the OLS coefficient if it is

negative); these fractions are then doubled to obtain two-tailed p-values. Each repetition uses

sample firms ARs from nine random non-event days selected from 1992. This procedure

maintains the cross-sectional correlation structure of firms returns in the non-event period so

that one can assess whether the event returns, which are also cross-correlated, are truly unusual.

Results

The results in Table 7 show that the specific objections are generally positively related to

abnormal returns but are not significant, with one exception. Object1 (comments on an

exemption for small firms) is significantly negative (bootstrapped p = 0.01), indicating that the

rules are anticipated to be harmful to those small firms. The insignificance of Object2 to Object

8 is largely due to multicollinearity of these variables, since Models 2 and 3 show that Common

factor and Nobject are significantly positively related to abnormal returns (bootstrapped p = 0.02

for both variables). This evidence of a positive relationship between abnormal returns and the

degree of objection is consistent with the Governance Improvement Hypothesis. The negative

coefficient on Object1 is consistent with the Disclosure Cost Hypothesis, but it must be

recognized that only nine small firms raised concerns in this area so the conclusion is limited to

such firms.

4.4 Using Stock Returns to Predict Lobbying Behavior in the Cross-section

The analysis in the previous section examines the contemporaneous association of returns with

lobbying activity. It should be noted that all comment letters were submitted after Event 1, and

with few exceptions, after the proposals release in Event 2. A natural question that arises is, can

25

one use stock price movements in response to Event 1 to predict managers lobbying activity that

followed? Hypothesis 1 predicts that firms experiencing the most positive stock price reactions

to the proposed regulation have executives who will then resist the rule change the most, because

anticipated governance improvements will diminish rents being earned by the manager. In

contrast, Hypothesis 2 predicts that more lobbying will follow more negative stock returns.

The regression model used to investigate this issue is as follows:

iiiMatchii SizedARdARddNobject m++++= 3,210 (5)

where ARi is the three-day cumulative abnormal return for firm i during Event 1, ARMatch,i is the

corresponding return for the firm in the control sample that matches firm i, and Sizei is the

natural log of market value at the end of 1991. The latter two variables are used as controls.

ARMatch,i is included to verify that any significant association between lobbying activity and ARi

is not also present for control firms that did not lobby (i.e. to ensure that the result is not spurious

because, for example, lobbying is related to firm characteristics that are correlated with risk

factors or industry specific news). Sizei controls for the fact that large firms tend to lobby more,

because they have the staff and resources to prepare submissions to the SEC.

In addition, since the dependent variable is discrete, a linear regression is possibly

misspecified. To check the robustness of the results, an ordered logit equation is also estimated:

7...,,1),()Pr( 10,981

=+++= =

jSizefARfARffFJNobject iiMatchiJ

jji (6)

where f1, , f7 are the intercept and cutoff values between the seven categories F(x) = ex / (1+ex) is the logistic distribution function.

While the analysis in this section is by no means independent of that of the previous section (the

three-day returns here are subsets of the nine-day returns), the interpretation is distinctly different

because the timing of Event 1 precedes any lobbying.

26

Results

Table 8 contains the results of estimating equations (5) and (6) in Panels A and B, respectively.

Model 1 shows that the abnormal returns during Event 1 does predict the subsequent lobbying

activity in the direction predicted by the Governance Improvement Hypothesis. Firms

experiencing more positive abnormal returns are likely to submit more objections to the SEC

(OLS coefficient on AR = 0.15, t = 4.16, p < 0.001). Model 2 shows that it is unlikely for this

result to be spurious; there are no significant associations between the lobbying intensity with

returns of matching control firms. The inferences using the ordered logit estimation are similar

(coefficient = 0.19, c2 = 17.33, p < 0.001).

5. Discussion and Conclusion

This paper used several complementary approaches to assess the impact of the SECs regulation

of executive compensation disclosure. First, lobbying companies experienced abnormally high

stock returns over the 8-month event period. By itself, this would be only weak evidence for the

Governance Improvement Hypothesis. Corroborating this result is the finding that operating

performance of lobbying firms was on average lower before the regulation, and subsequently

improved to match or exceed that of the control firms. Also, cross-sectional variations in

abnormal returns in events days are positively associated with the extent of lobbying activity.

Finally, one could have used abnormal returns in reaction to the SEC Chairmans announcement

of impending regulation to help predict the extent of companies lobbying efforts; more lobbying

tended to follow higher returns. Taken as a whole, these results are consistent with the

Governance Improvement Hypothesis: investors and managers anticipated that the regulation

would improve corporate governance if implemented. In contrast, there is no evidence for the

Disclosure Cost Hypothesis, with the exception of negative abnormal returns for the nine

27

smallest firms.

In light of these results, it may seem puzzling why the short-window tests did not detect

any differences in the average stock returns between samples. One plausible explanation is that

there were unrelated confounding events that affected the mean. This is a general concern with

event studies with aligned calendar times, as confounding events could either cause spurious

results (which is the more common concern) or make the effect of interest undetectable. In the

same vein, the evidence over longer periods should be interpreted with some caution. In

particular, it is difficult to make strong inferences of cause-and-effect using the long-horizon

returns and changes in operating performance over six years, as other events occurring in the

intervening period could have been responsible for the changes. Nevertheless, these results are

consistent with the results of short-window cross-sectional analyses of returns, with which

stronger inferences can be made because it is less likely for confounding events to generate

cross-sectional variations in returns that coincide with the variation in lobbying.

This study examined a change in regulation that affects disclosures not directly

recognized on the financial statements. This focus departs from existing studies, which have

thus far examined changes in regulations that impact the accounting statements. The evidence

showed that disclosure regulation did have substantive economic consequences for producers

and consumers of that information. Other disclosure regulations such as those concerning

segmented reporting and the use of derivatives could provide fruitful research opportunities.

28

References Aboody, D., R. Kasznik, and M. Williams, 2000. Purchase versus pooling in stock-for-stock

acquisitions: why do firms care? Journal of Accounting and Economics 29, 261-286.

Ali, A. and S. Kallapur, 2001. Securities price consequences of the Private Securities Litigation Reform Act of 1995 and related events. The Accounting Review 76, 431-460

Barber, B.M. and J.D. Lyon, 1996. Detecting abnormal operating performance: the empirical power and specification of test statistics. Journal of Financial Economics 41, 359-399.

Barber, B.M. and J.D. Lyon, 1997. Detecting long-run abnormal stock returns: the empirical power and specification of test statistics. Journal of Financial Economics 43, 341-372.

Bernard, V.L., 1987. Cross-sectional dependence and problems in inference in market-based accounting research. Journal of Accounting Research 25, 1-48.

Dechow, P., A. Hutton, and R. Sloan, 1996. Economic consequences of accounting for stock-based compensation. Journal of Accounting Research 34 Supplement, 1-20.

Diekmann, K.A., 1997. "Implicit justification" and self-serving group allocations. Journal of Organizational Behavior 18, 3-16.

Dye, R.A., 1985. Disclosure of nonproprietary information. Journal of Accounting Research 23, 123-145.

Fama, E.F. and K. French, 1992. The cross-section of expected stock returns. Journal of Finance 47, 427 465.

Fields, T., T. Lys, and L. Vincent, 2001. Empirical research on accounting choice. Journal of Accounting and Economics 31, 255-307.

Grossman, S.J., 1981. The informational role of warrantees and private disclosure about product quality. Journal of Law and Economics 24, 461-83.

Hayes, R.M. and S. Schaefer, 1999. How much are differences in managerial ability worth? Journal of Accounting and Economics 27, 125-148.

Hayes, R.M. and S. Schaefer, 2000. Implicit contracts and the explanatory power of top executive compensation for future performance. RAND Journal of Economics 31, 273-293.

Holthausen, R.W. and D.F. Larcker, 1996. The financial performance of reverse leverage buyouts. Journal of Financial Economics 42, 293-332.

Jensen, M.C., 1993. The modern industrial revolution, exit, and failure of internal control systems. Journal of Finance 48, 831-880.

Jensen, M.C. and K.J. Murphy, 1990. Performance pay and top-management incentives. Journal of Political Economy 98, 225-264.

Johnson, T.S., 1995. Pay for Performance: Corporate executive compensation in the 1990s. Delaware Journal of Corporate Law 20, 183-224.

Kothari, S.P. and J.B. Warner, 1997. Measuring long-horizon security price performance. Journal of Financial Economics 43, 301-339.

29

Krasker, W.S., E. Kuh, and R.E. Welsch, 1983. Estimation for dirty data and flawed models. Chapter 11 in Handbook of Econometrics Volume 1, 652-698.

Leftwich, R., 1980. Market failure fallacies and accounting information. Journal of Accounting and Economics 2, 193-211.

Leftwich, R., 1981. Evidence of the impact of mandatory changes in accounting principles on corporate loan agreements. Journal of Accounting and Economics 3, 3-36.

Longstreth, B. and N. Kane, 1992. Shareholders' growing role in executive compensation. New York Law Journal 207 n33.

Lys, T. and L. Vincent, 1995. An analysis of value destruction in AT&T's acquisition of NCR. Journal of Financial Economics 39, 353-378.

McGahran, K.T., 1988. SEC disclosure regulation and management perquisites. The Accounting Review 63, 23-41.

Milgrom, P.R., 1981. Good news and bad news: Representation theorems and applications. The Bell Journal of Economics 12, 380-391.

Quinn, L.C., 1995. Proceedings of the 1994 Corporate Law Symposium. University of Cincinnati Law Review 63, 769-815.

Salwen, K.G., 1992. SEC unveils new rules on disclosures of corporate executives' pay packages. Wall Street Journal, February 14, 1992, A4.

Sefcik, S.E. and R. Thompson, 1986. An approach to statistical inference in cross-sectional models with security abnormal returns as dependent variable. Journal of Accounting Research 24, 316 - 334.

Sloan, R.G., 1993. Accounting earnings and top executive compensation. Journal of Accounting and Economics 16, 55-100.

U.S. Securities and Exchange Commission, 1983. Disclosure of Executive Compensation (Final Rules). Release No. 33-6486. Federal Register 48 No. 190, 48 CFR 44467- 75.

U.S. Securities and Exchange Commission, 1992. Executive Compensation Disclosure (Proposed Rules). Release No. 33-6940. Federal Register 57 No. 128, 57 CFR 29582 - 608.

U.S. Securities and Exchange Commission, 1992. Executive Compensation Disclosure (Final Rules). Release No. 33-6962. Federal Register 57 No. 204, 57 CFR 48126 - 56.

U.S. Securities and Exchange Commission Archives. Letters to the Commission on Release No. 33-6940. File 57-16-92. Washington, D.C.

Verrecchia, R.E., 1983. Discretionary disclosure. Journal of Accounting and Economics 5, 179-194.

Wagenhofer, A., 1990. Voluntary disclosure with a strategic opponent. Journal of Accounting and Economics 12, 341-363.

Zimmerman, J.L., 2003. Accounting for Decision Making and Control, 4th Edition. McGraw-Hill.

30

Table 1: Sample selection and financial statistics

Panel A: Construction of comment letter sample and control sample Total number of firms submitting comments to SEC 210 Firms not found on CRSP database or have missing returns - 15

Number of comment letter firms in stock return analyses 195 Observations without matching firms - 5

Number of firms in comment letter sample / control sample 190 Number of firms with 4-digit SIC match 133 Number of firms with 3-digit SIC match 21 Number of firms with 2-digit SIC match 36

Total 190 Panel B: Financial statistics (for 1991 fiscal year-end)

n Mean S.D. Lower

Quartile Median Upper

Quartile

Comment letter sample

Total assets ($ millions) 190 16,255 ** 32,689 1,608 5,221 ** 13,326 Book value of equity ($ millions) 190 3,294 *** 4,983 431 1,515 ** 4,163 Market value of equity ($ millions) 190 6,953 ** 11,904 710 2,572 6,536

Book-to-market equity 190 0.68 ** 0.46 0.44 0.61 *** 0.79 Leverage 190 0.65 0.18 0.54 0.65 0.75 Return on assets (%) 187 4.35 7.65 0.87 3.62 5.75 Return on equity (%) 187 8.99 20.90 3.57 11.15 * 15.16

Control sample

Total assets ($ millions) 190 8,496 16,883 792 3,359 8,656 Book value of equity ($ millions) 190 1,638 1,860 321 959 2,396 Market value of equity ($ millions) 190 4,209 6,464 669 2,159 5,181

Book-to-market equity 190 0.57 0.50 0.32 0.53 0.71 Leverage 190 0.61 0.21 0.49 0.62 0.76 Return on assets (%) 189 4.70 8.91 1.10 3.98 7.53 Return on equity (%) 185 10.61 17.52 5.02 13.07 17.30 Notes for Table 1: Total assets is Compustat item 6. Book value of equity is item 60. Market value of equity is year-end price (item 199) multiplied by shares outstanding (item 25). Leverage is total liabilities (data181) divided by total assets (item 6). Three control firms with leverage > 1 winsorized to 1. Return on assets (ROA) is income before extraordinary items (item 18) plus after-tax interest (item 15 x (1 - tax rate)) divided by average assets (item 6). Tax rate is calculated as income taxes (item 16) divided by pretax income (item 170). Return on equity (ROE) is income before extraordinary items (item 18) divided by average equity (item 60). Three (one) firms in comment letter (control) sample with missing lag assets are excluded. A further four observations in the control sample with negative equity are excluded from ROE calculations. One (two) observations in comment letter (control) sample with ROE < -1 winsorized to -1. *, **, *** denote two-tail significance levels for between sample differences, at the 5%, 1%, and 0.1% level, respectively, using two-sample t-tests (for mean) and two-sample Wilcoxon rank sums test (for median).

31

Table 2: Descriptive statistics on content of comment letters (n = 195)

Panel A: Cross-tabulation of objections to proposal

Obj

ect1

Obj

ect2

Obj

ect3

Obj

ect4

Obj

ect5

Obj

ect6

Obj

ect7

Obj

ect8

Opp

ose

Object1 9

Object2 3 117 Object3 5 94 146 Object4 1 27 35 40 Object5 2 77 96 30 121 Object6 5 71 93 29 73 103 Object7 0 12 16 7 13 14 16 Object8 0 18 20 9 17 19 6 20 Oppose 8 112 130 35 102 95 16 20 160

Panel B: Conditional probability of objections given another objection

Conditional Probability of Second Objection

Conditioning Variable O

bjec

t1

Obj

ect2

Obj

ect3

Obj

ect4

Obj

ect5

Obj

ect6

Obj

ect7

Obj

ect8

Opp

ose

Object1 1.00 0.33 0.56 0.11 0.22 0.56 0.00 0.00 0.89 Object2 0.03 1.00 0.80 0.23 0.66 0.61 0.10 0.15 0.96 Object3 0.03 0.64 1.00 0.24 0.66 0.64 0.11 0.14 0.89 Object4 0.03 0.68 0.88 1.00 0.75 0.73 0.18 0.23 0.88 Object5 0.02 0.64 0.79 0.25 1.00 0.60 0.11 0.14 0.84 Object6 0.05 0.69 0.90 0.28 0.71 1.00 0.14 0.18 0.92 Object7 0.00 0.75 1.00 0.44 0.81 0.88 1.00 0.38 1.00 Object8 0.00 0.90 1.00 0.45 0.85 0.95 0.30 1.00 1.00 Oppose 0.05 0.70 0.81 0.22 0.64 0.59 0.10 0.13 1.00 Notes for Table 2: Diagonal entries in Panel A indicate the total number of each type of objection. Off-diagonal elements indicate the number of instances in which both types of objections were observed. Panel B should be read across, row by row, so that each entry in the first row, for example, shows the probability of observing an objection given Object1=1. Object1 = 1 if a firm asked the SEC to provide less onerous provisions for smaller firms (and 0 otherwise). Object2 = 1 if a firm stated that the compliance costs would be too high. Object3 = 1 if a firm objected to the inclusion of a report from the compensation committee. Object4 = 1 if a firm said that a dollar value should not be attached to stock option grants. Object5 = 1 if a firm disagreed with the disclosure of future stock option values using hypothetical returns. Object6 = 1 if a firm did not want a graph depicting the performance of the company. Object7 = 1 if a firm disagreed with the disclosure of options with lowered strike prices. Object8 = 1 if a firm objected to the disclosure of interlocks in the compensation committee. Oppose = 1 if a firm express an overall negative opinion of the proposed regulation

32

Table 3: Results of Maximum Likelihood Factor Analysis

Panel A: Analysis of available factors

Factor 1 2 3 4 5 6 7 Eigenvalue 1.84 0.21 0.06 0.01 0.05 0.09 0.14 c2 d.f p-value Test of H0: no common factors 107.67 21 < 0.01 Test of H0: one factor is sufficient 7.38 14 0.92

Panel B: Factor identification

Simple correlation of factor 1 with Nobject = =8

2)(

jjObject 0.97

Notes to Table 3: This table shows the results of an analysis of common factors for seven variables, Object2 through Object8. The number of observations is 195.

33

Table 4: Analysis of market reaction to events for comment letter and control samples

Comment Letter Sample

(n = 190) Control Sample

(n = 190) Difference # Days Mean t-stat Mean t-stat Mean t-stat

Panel A: Market-model-excess returns

Event 1 3 0.36 1.57 0.33 1.31 0.02 0.07 Event 2 3 -0.19 -0.78 -0.16 -0.72 -0.03 -0.07 Event 3 3 0.24 0.91 0.20 0.70 0.04 0.09 All 3 events 9 0.43 0.94 0.40 0.81 0.03 0.04 Panel B: Raw Returns Event 1 3 0.36 1.65 0.29 1.16 0.07 0.22 Event 2 3 -0.12 -0.50 -0.13 -0.57 0.02 0.05 Event 3 3 1.66 5.95 1.67 5.66 -0.01 -0.02 All 3 events 9 1.93 4.23 1.86 3.75 0.08 0.11 Notes for Table 4: All returns are shown in percent. The estimation period for the market model is the 200 trading days preceding Event 1, using value-weighted returns as the market index. Differences are computed as mean of comment letter sample - mean of control sample or population. Inferences are similar with bootstrapped p-values or using median returns. Event 1 is the announcement by SEC Chairman Breeden on February 13, 1992. Event 2 is the release of the proposed rules on June 23, 1992. Event 3 is the adoption of the final rules on October 16, 1992.

34

Table 5: Results from regression of long-window cumulative abnormal returns on the decision to comment

iiiCommenti edBMcSizebDaCAR ++++= , (1)

n = 379 Predicted Signs Model 1 Model 2 H1 H2 Coeff t-stat Coeff t-stat Returns accumulated from Event 1 to Event 2 (93 days) Intercept none none -1.95 -1.27 -34.59 -5.82 DComment + - 5.82** 2.68 4.90* 2.36 Size none none 3.12*** 4.57 BM none none 16.26*** 6.24 R2 and p-value 1.87% 0.01 12.67%

35

Table 6: Analysis of Operating Performance (returns in %)

ROA ROE

Year

Com-ment

Sample Control Sample

Differ-ence

t- or Z- statistic n1 n2

Com-ment

Sample Control Sample

Differ-ence

t- or Z- statistic n1 n2

Panel A: Means of performance ratios and t-statistic

1990 4.79 5.93 1.14 1.50 184 188 13.01 14.27 1.26 0.75 180 184 1991 4.35 4.70 0.35 0.41 187 189 8.99 10.61 1.62 0.81 187 185 1992 4.44 5.44 1.00 1.37 189 188 11.10 13.01 1.91 1.21 189 184 1993 4.67 5.51 0.84 1.19 184 188 11.51 12.98 1.47 0.98 184 183 1994 5.99 5.68 0.31 0.40 180 186 14.44 13.38 1.06 0.60 180 181 1995 6.81 5.53 1.28 1.24 172 181 15.90 14.21 1.69 0.92 171 177

DD 90-95 0.68 0.30 0.98 1.39 168 179 2.73 0.73 3.46* 1.94 164 173

Panel B: Medians of performance ratios and Z-statistic for Wilcoxon rank sum test

1990 4.15 4.87 0.72** 2.82 184 188 12.79 14.37 1.58** 2.43 180 184 1991 3.62 3.98 0.36 1.64 187 189 11.15 13.07 1.92* 2.03 187 185 1992 3.74 4.23 0.49 1.56 189 188 12.33 13.12 0.79 1.42 189 184 1993 3.89 4.24 0.35 1.39 184 188 13.71 13.10 0.61 0.09 184 183 1994 4.38 3.97 0.41 0.54 180 186 14.71 14.00 0.71 1.13 180 181 1995 4.93 4.16 0.77 1.15 172 181 15.18 14.33 0.85 1.83 171 177

DD 90-95 0.39 0.13 0.52** 2.63 168 179 1.75 0.66 2.41** 2.87 164 173

Panel C: Aggregate measures of performance

1990 2.68 2.72 0.04 see 184 188 12.65 13.65 1.00 see 184 188 1991 1.70 2.11 0.41 notes 187 189 8.18 10.70 2.52 notes 187 189 1992 1.93 2.40 0.47 below 189 188 9.95 12.25 2.30 below 189 188 1993 2.18 2.66 0.48 184 188 12.27 14.09 1.82 184 188 1994 3.23 2.83 0.40 180 186 18.38 15.52 2.86 180 186 1995 3.45 2.93 0.52 172 181 18.82 16.44 2.38 172 181

DD 90-95 0.77 0.21 0.56 168 179 6.17 2.79 3.38 168 179 Notes to Table 6: Return on assets (ROA) is income before extraordinary items (Compustat item 18) plus after-tax interest (item 15 (1-tax rate)) divided by average assets (item 6). Tax rate is calculated as income taxes (item 16) divided by pretax income (item 170). Return on equity (ROE) is income before extraordinary items (item 18) divided by average equity (item 60). |ROA| or |ROE| > 1 are winsorized to 1. Firm-years with negative equity have been excluded from the calculation of firm level ROE, but included for aggregate ROE. Aggregate ROA is the sum of the sample firms income before extraordinary items plus after-tax interest divided by the sum of average assets. Aggregate ROE is the sum of income before extraordinary items divided by the sum of average equity. No significance tests are available for the aggregate measures since these are samples of one. *, ** denote two-tail significance at the 5% and 1% levels. Note that the means/medians for D90-95 in Panels A and B are calculated on samples where both the 1990 and 1995 ROA (or ROE) are available, so they do not equal the changes in the means/medians of the yearly results reported in the table.

36

Table 7: Association of Abnormal Returns with Lobbying Activity (with asymptotic and bootstrapped p-values below coefficients)

Model 1: iij iji eOpposeajObjectaaAR +++= = 98

10)( (2)

Model 2: iiiii eOpposebfactorCommonbObject1bbAR ++++= 3210 . (3)

Model 3: iiiii eOpposecNobjectcObject1ccAR ++++= 3210 . (4)

Predicted Signs

Variable H1 H2

Model 1 (Specific

Objections)

Model 2 (Common Factor)

Model 3 (Sum of

Objections) Intercept none none -0.81 0.40 -1.18 0.46, 0.43 0.68, 0.91 0.23, 0.25 Common factor + - 1.04* (for Object2, Object8) 0.05, 0.02 Nobject + - 0.59* (= Object2 + + Object8) 0.03, 0.02 Object1 (Size) + - -4.13* -4.39* -4.14*

0.05, 0.01 0.03, 0.01 0.04, 0.01 Object2 (Compliance cost) + - 0.39 0.66, 0.33 Object3 (Committee report) + - -0.95 0.34, 0.14 Object4 (Option valuation) + - 1.37 0.17, 0.04 Object5 (Appreciation Rates) + - 0.95 0.25, 0.11 Object6 (Performance Graph) + - 0.82 0.35, 0.08 Object7 (Option Repricing) + - 0.14 0.92, 0.99 Object8 (Committee Interlocks) + - 1.13 0.41, 0.19 Oppose + - 0.16 -0.14 -0.304 0.88, 0.91 0.90, 0.80 0.78, 0.62 n = 193 R2 (%) 7.030 4.880* 5.320* P-value 0.138, 0.271 0.024, 0.062 0.016, 0.049 Notes for Table 7: OLS dependent variable is the percentage market-model excess return, computed daily and accumulated over nine days. Asymptotic and bootstrapped p-values are respectively presented below coefficients. The White test fails to reject the assumption of homoscedasticity. From a preliminary regression of Model 3 with 195 observations, two outliers have been deleted based on |DFFITS| > 3(k/n)1/2, where k = 4 = the number of parameters (Krasker, Kuh, and Welsch, 1983). *, ** denote two-tail significance at the 5% and 1% level, respectively. Boots trapped p-values are calculated as follows: one-tailed p-values are the proportions of 10,000 repetitions that generated coefficients greater (less) than the positive (negative) coefficient in the table; doubling these fractions results in two-tailed p-values. Each repetition selects nine random non-event days in 1992 without replacement.

37

Table 8: Prediction of the number of objections using abnormal returns surrounding Event 1

Panel A: OLS estimation

iiiMatchii SizedARdARddNobject m++++= 3,210 (5)

Predicted

Signs Model 1 Model 2

Variable H1 H2 Estimated Coefficient t-statistic

Estimated Coefficient t-statistic

Intercept none none 1.87*** 3.64 1.93*** 3.75 AR + -- 0.15*** 4.16 0.15*** 4.24 ARMatch none none -0.04 -1.16 Size none none 0.12 1.90 0.12 1.78

(n = 187) R2 10.86% 11.51%

Panel B: Ordered Logit estimation

7...,,1),()Pr( 10,981

=+++= =

jSizefARfARffFJNobject iiMatchiJ

jji (6)

Predicted

Signs Model 1 Model 2

Variable H2 H2 Estimated Coefficient c2 statistic

Estimated Coefficient c2 statistic

Intercepts none none - 6.05 0.16 - 5.98 0.29 to 1.84 to 40.97 to 1.92 to 39.94 AR + -- 0.19*** 17.33 0.19*** 17.86 ARMatch none none -0.04 0.93 Size none none 0.16 4.41 0.15* 3.89

Concordant Pairs 55.4% 56.5% (n = 187) Discordant Pairs 31.9% 32.2% Notes for Table 8: Nobject is the number of objections received from each firm in the comment letter sample, excluding the objection lobbying for a size exemption (Object1). ARi is the 3-day market-model excess return around Event 1 for firm i in the comment letter sample. ARMatch,i is the 3-day market-model excess return around Event 1 for the control matched to firm i. In a preliminary regression of model 2 with 190 observations, 3 outliers have been deleted based on |DFFITS| > 3(k/n)1/2, where k = 4 = the number of parameters (Krasker, Kuh, and Welsch., 1983). *, **, and *** denote respectively two-tail significance levels of 5%, 1%, and 0.1%.

38

Figure 1: Long-Window Cumulative Market Model Excess Returnsin Pre-Event, Event, and Post-Event Period

- Comment Letter Sample Less Control Sample Returns -

-10

-5

0

5

10

7-Jun-91

7-Aug-91

7-Oct-91

7-Dec-91

7-Feb-92

7-Apr-92

7-Jun-92

7-Aug-92

7-Oct-92

7-Dec-92

7-Feb-93

7-Apr-93

7-Jun-93

Date

Ave

rage

Cu

mu

lati

ve A

bn

orm

al R

etu

rn (

Per

cen

t)

Difference in Mean CARDifference in Median CAR

Event 2 Jun 23

Event 1 Feb. 13

Event 3 Oct. 16

Event PeriodPre-event Period Post-event Period

39

Figure 2a: Difference in Average Operating Performance Over Time- Comment Letter Sample Less Control Sample -

(Source: Table 6, Panels A and B)

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

1990 1991 1992 1993 1994 1995

RO

A

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

RO

E

Mean ROA Median ROA

Mean ROE Median ROE

Figure 2b: Difference in Aggregate Measures of Performance Over Time- Comment Letter Sample Less Control Sample -

(Source: Table 6 Panel C)

-0.75%

-0.50%

-0.25%

0.00%

0.25%

0.50%

0.75%

1990 1991 1992 1993 1994 1995

RO

A

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

RO

E

ROA

ROE