Forthcoming in the Journal of Business Finance and Accounting

Corporate Financial Control Mechanisms and Firm Performance: The Case of Value-Based Management Systems

Harley E. Ryan, Jr. Emery A. Trahan (Corresponding Author) Georgia State University Northeastern University Robinson College of Business College of Business Administration Department of Finance, 4 Finance Group, 413 Hayden Hall Atlanta, GA 30303 Boston, MA 02115 Tel: (404) 651-2674 Tel: (617) 373-4568 Email: cryan@gsu.edu Email: e.trahan@neu.edu

September 13, 2006

Abstract We examine the performance of 84 firms that adopt value-based management (VBM) systems during the period 1984-1997. The typical firm significantly improves matched-firm-adjusted residual income after adopting VBM. This improvement persists for the five post-adoption years studied. After controlling for possible sample bias, we find that large firms show less improvement than small firms. We find a negative relation between tying compensation to VBM and post-adoption performance. We also find that firms reduce capital expenditures following VBM adoption, but that the reductions in spending do not differ based on the firms’ growth opportunities. Overall, the evidence suggests that VBM improves economic performance and the efficient use of capital.

JEL classifications: M41; J33; G34

Keywords: Value-based management; Residual income; Management compensation, Corporate governance

We appreciate helpful comments from Olubunmi Faleye, Sherry Jarrell, Shane Johnson, Jayant Kale, Omesh Kini, John Martin, Sheila Ryan, James Wallace, Sam Weaver, J. Fred Weston, Roy Wiggins, the Editor (Pete Pope) and an anonymous referee. We acknowledge the excellent research assistance of Pingshun Huang, Huihua Li, Roy Song and Lingling Wang. We are responsible for remaining errors.

Forthcoming in the Journal of Business Finance and Accounting

Corporate Financial Control Mechanisms and Firm Performance: The Case of Value-Based Management Systems

1. Introduction

Effective corporate governance and financial control includes the use of monitoring and

incentive mechanisms to align divergent interests between shareholders and managers and

encourage the creation of shareholder value. Value-based management systems (VBM) provide

an integrated management strategy and financial control system intended to increase shareholder

value by mitigating agency conflicts. In concept, VBM reduces agency conflicts and helps

create shareholder value since it reveals value-increasing decisions to employees, allows for

easier monitoring of managers’ decisions, and provides a method to tie compensation to

outcomes that create shareholder value. However, the degree to which VBM systems actually

improve the economic performance of publicly held firms is an open question. To gain insight

into this issue, we examine the use and economic efficacy of value-based management systems

by 84 firms that adopt VBM systems from 1984 to 1997. We investigate two related research

questions: (1) Does the adoption of a VBM system improve economic performance? and (2)

What factors enhance or hinder the effectiveness of VBM systems?

Our primary goal is to examine whether the adoption of a VBM system improves

economic performance. We recognize that firm performance and the decision to tie

compensation to a VBM metric can be endogenous, which creates a potential sample selection

bias. For instance, firms that are performing poorly face tougher challenges to creating

economic value and could be more likely to tie compensation to VBM to provide managers the

incentives to overcome these challenges. Alternatively, managers who expect to achieve a

certain level of performance can negotiate a compensation contract based on the VBM metric

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that essentially assures a bonus payout. Our sample includes firms that base compensation on

VBM metrics, but also firms that use VBM for analysis and evaluation only. Thus, we can

examine why firms choose to tie compensation to VBM, which allows us to control for potential

sample selection bias that results from endogenous relations between compensation plans and

firm performance.

With regard to our first research question, we find that firm performance increases

following the adoption of value-based management systems. Compared to a matched firm based

on industry, prior performance, and size, firms that adopt VBM systems increase residual income

for the five years subsequent to the adoption of a VBM system relative to the year before

adoption. The median firm in our sample increases industry- and performance-adjusted residual

income divided by invested capital by over 7 percentage points for the five-year period

subsequent to VBM adoption. We do not find evidence that VBM encourages underinvestment

in high-growth firms, suggesting that the improvement in residual income does not come at the

expense of long-term value.

With regard to our second research question, we find that firm size is the only firm-

specific characteristic that relates to the effectiveness of VBM in all years. After controlling for

possible sample selection bias, we find that large firms show less improvement than small firms.

We note, however, that these regressions have low adjusted R2s. Possibly, this result suggests

that larger firms face greater monitoring costs, which make it more difficult to implement

programs in larger firms. In our multivariate analysis, we also find that (i) firms that perform

better prior to adoption are more likely to tie compensation to VBM and (ii) the post adoption

adjusted performance in the first two years after adoption negatively relates to the use of VBM to

determine compensation. This effect is not statistically significant after two years. It is possible

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that firms that already focus on value creation are more likely to tie VBM to compensation and

that these firms simply have less potential for improvement. Alternatively, firms might cap

bonus payouts too low when they implement plans, which reduces initial efficacy. We also find

that firms reduce capital expenditures following VBM adoption. These reductions in spending

do not differ based on the firms’ growth opportunities. Thus, the improvement in performance

does not appear to come at the expense of long-term value.

Overall, our results provide support that value-based management systems are effective

mechanisms for improving corporate performance. The remainder of the paper is organized as

follows. In Section 2 we provide a summary of the value-based management literature and the

objectives of this research. In Section 3 we describe the sample and research method. Section 4

contains the empirical results. In Section 5 we provide a summary and conclusion.

2. Value-Based Management

The literature on property rights (e.g., Alchian and Demsetz, 1972) and agency theory

(e.g., Jensen and Meckling, 1976) maintains that different incentives lead to conflicts between

shareholders and managers of the public firm that result in a loss in firm value. Ultimately, the

shareholders bear this loss. Value-based management provides an integrated management

strategy and financial control system designed to mitigate these agency conflicts and increase

shareholder value.1 VBM systems attempt to accomplish this goal by providing managers with a

set of decision-making tools (metrics) that, at least in theory, identify which alternatives create or

destroy value, and often by linking compensation and promotions to shareholder value. Firms

can use these metrics to monitor and reward management performance. They provide a

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mechanism for linking managers’ decisions to firm performance outcomes that create

shareholder value and provide a means to further align shareholder and managerial interests.

Value-based management has captured the interest of the corporate and investment

communities. Ryan and Trahan (1999) report that 87 percent of 86 CFOs surveyed indicate that

they are familiar with value-based management. Most of these CFOs also indicate that their firm

uses one or more VBM systems. This interest has also been demonstrated in the business press.

Articles in Fortune (e.g., Stires, 2001; Colvin, 2000; Tully, 1999) include a list of 1,000

companies ranked by how much market value they added during the past decade, based on Stern

Stewart’s market value added (MVA) metric. These articles profile several corporations that

have adopted a variety of management systems, based on different VBM metrics, in an effort to

increase their value.

We identify four variations of VBM metrics from these articles in the popular press. All

of the metrics are similar in that they are single-period measures of performance that take into

account return on invested capital and the relevant cost of capital.2 They are all consistent with

discounted cash flow valuation. Although consulting firms have popularized these metrics (for

example, EVA® is widely adapted and marketed by Stern Stewart & Co., who have trademarked

the name), many companies apply their own versions of the metrics. We do not take any given

metric to represent the work of a consulting firm that may have popularized the method. We

provide a summary of these four metrics below.

1 See Ittner and Larcker (2001) for a general discussion of the VBM model and a thorough review of the empirical research in managerial accounting from a VBM perspective. 2 Note that ROIC does not directly consider cost of capital in calculating the metric; however, the metric is compared to the cost of capital to evaluate performance.

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Discounted Cash Flow (DCF)—DCF methods, for instance shareholder value added (SVA), express value as expected future cash flows discounted to the present time at the company’s cost of capital. See Rappaport (1998) for a more detailed discussion of DCF methods as they relate to value-based management.

Cash Flow Return on Investment (CFROI)—CFROI expresses an estimate of a company’s single-period cash flow as a percentage of total investment. Madden (1999) provides a detailed discussion of CFROI.

Return on Invested Capital (ROIC)—ROIC is defined as the ratio of net operating profits less adjusted taxes (NOPLAT) to invested capital. See Copeland, et al., (2000) for a more detailed discussion of ROIC.

Residual Income (RI)—RI measures the excess earnings over a capital charge based on investment opportunities of similar risk. Stern Stewart & Co. popularized RI under the market name of EVA®. See Wallace (1997) for a more detailed discussion of RI.

Despite the attention afforded value-based management techniques and their widespread

application, we have scant evidence on their ability to improve firm performance. Much of the

existing empirical research, often conducted by the consulting firms who market value-based

management systems, focuses on the relations between the metrics (or value-drivers) and

shareholder value. These studies by consultants (e.g., Stewart, 1994) document positive relations

between performance metrics [e.g., Economic Value Added (EVA®)] and historical stock-price

performance. In contrast, an academic study by Biddle, et al., (1997) concludes that EVA®

explains shareholder returns no better than earnings. Copeland (2002) argues that

contemporaneous measures of common value-based management metrics do not do a good job

of explaining changes in stock prices, and that changes in expectations need to be considered.

Copeland’s argument underscores the difficulty in testing for any relation between the use of

VBM systems and stock price improvement.3

3 See also Martin and Petty (2000) for a discussion of value-based management and popular VBM metrics and Fabozzi and Grant (2004) for a discussion of applications of VBM metrics to security analysis.

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Wallace (1997) examines the impact of residual income-based compensation plans on

observable measures of managers’ behavior, which avoids the problems associated with

analyzing stock-price improvement. He limits his sample to firms that adopt residual-income-

based (EVA®-type) performance measures for compensation purposes. His results show that,

relative to a group of control firms, managers whose compensation is tied to residual income

reduce capital investment, increase payouts to shareholders, and use their assets more intensely.

In a follow-up study, Wallace (1998) surveys firms that use EVA for management control and

management compensation and firms that use EVA only for management control purposes. He

finds that firms tying compensation to EVA implement the value-based management systems

more fully into the organization. Wallace studies these firms through survey responses only, and

does not conduct any other empirical testing. Riceman, et al., (2002) study managers within a

single firm and find that bonuses tied to EVA do not improve performance more than bonuses

tied to accounting measures, after controlling for other factors.

Hogan and Lewis (2005) study a sample of firms that adopt economic profit (EVA®-type)

plans linked to executive compensation, between 1983 and 1996. They find that adopting firms

experience post-adoption improvements in operating performance relative to past performance,

but that the improvements are not substantially different than those realized by non-adopting

control firms. Segmenting EVA adopters into those expected to adopt a plan and those that are

surprises, they find that the plans work best for firms that are expected to adopt.

Wallace (1997) and Hogan and Lewis (2005) develop their samples of firms through a

search of the financial press and proxy statements for firms tying compensation to residual

income or economic profit. As noted by Zimmerman (2001), a lack of good publicly available

data provides an obstacle to researchers who seek to test theories suggested from practice. To

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gain more useful data, we obtain our sample through a survey of 1,000 CFOs. Our method

allows us to gather information on a broader array of value-based management systems and

provides private information on how firms use these methods, which allows us to conduct

additional tests. Additionally, our sample allows us to address endogenous relations between

firm performance and governance systems.

Our sample selection method distinguishes our study from previous studies. These

relevant studies only have access to data on VBM compensation plans for top executives, and

exclude firms that use VBM systems for evaluation and budgeting (see Ittner and Larcker, 1998,

2001). Our sample includes firms that tie VBM to compensation, and also firms that use VBM

systems for evaluation, budgeting, and monitoring but not for compensation. This feature allows

us to explore the more extensive use of VBM, and also allows us to shed light on the endogenous

decision process. Our sample also allows us to examine a broader group of value-based

management systems (DCF, CFROI, ROIC, RI, and other methods), rather than only systems

based on the EVA®-type measures of residual income used in these prior studies.4

Our method makes three additional contributions. First, we extend our analysis of post-

adoption performance to five years beyond the year of adoption, allowing us to examine longer-

term effects. Second, we use different tax rates based on the statutory tax rate in effect for a

particular year as opposed to assuming a constant tax rate for all years. Third, we use a cost of

equity based on the capital asset pricing model, a cost of debt based on Moody’s bond yields,

and the firm’s capital structure to estimate a weighted average cost of capital for each of our

4 Wallace (1998) reports survey results for firms that do not tie compensation to EVA, but does not conduct any additional empirical testing. Hogan and Lewis (2005) in their Lexis/Nexis search, look for “economic value added, EVA, residual income, economic value management, and economic profit.” They may pick up non-EVA-type plans in this search, but most of these terms suggest EVA-type plans.

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sample firms, and for all firms with data available on COMPUSTAT, for matching purposes. 5

This contribution allows us to more accurately estimate residual income for each firm, to match

firms more accurately, and to examine the contribution of changes in the cost of capital on post-

adoption performance.

3. Data and Method

3.1. Data

We use data from Ryan and Trahan’s (1999) survey of CFOs of the 1,000 largest publicly

owned, industrial and non-financial services companies in the United States.6 The CFOs

provided information on whether their companies use any value-based management methods, the

year of adoption, and whether the firm ties executive compensation to the methods. We delete

firms that provide only approximate years of adoptions. From these responses, we obtain a

sample of 84 adoptions of value-based management systems between 1984-1997, with adoption

years and complete financial data available on Standard and Poor’s COMPUSTAT database.

Table 1 presents the distribution of adoptions by year and type of VBM system adopted.

Our approach is not survey research, but we rely on a survey to identify our sample of

VBM adoptions. Using a survey to identify our sample provides some distinct advantages.

Graham and Harvey (2001) note that the survey approach provides a balance between large

sample analysis and clinical studies, but that survey analysis poses the risk that the respondents

do not represent the population. We take several steps similar to Graham and Harvey (2001) to

5 Wallace (1997) assumes a 12% cost of capital for all firms. Hogan and Lewis (2005) use an asset beta and asset returns to estimate a cost of capital for each firm. 6 See Ryan and Trahan (1999) for a complete description of the survey method applied and description of the data. See also, Ryan and Trahan (2000).

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investigate whether response bias affects our results.

Ryan and Trahan (1999) guarantee anonymity to respondents to minimize the potential

for biased responses. Because questionnaires may be forwarded upon receipt by the CFO to

others within the company, each respondent was asked to write in his or her corporate title. We

only use responses from high-level corporate personnel who indicate that they had implemented

a VBM system and that identified a specific year for the implementation. We do not use

responses that indicate plans to implement VBM or that do not identify the year of adoption. To

check for response bias, we follow Moore and Reichert (1983) and compare characteristics of

responding firms to characteristics of the population at large. Tests indicate that the responding

firms are similar to non-responding firms across a variety of financial characteristics that

measure firm size, leverage, and profitability. For firms that indicate that they use VBM to

determine bonuses, we read proxy statements and verify that the firms do use a bonus plan as

part of their compensation scheme. However, firms are not required to disclose how they

determine bonuses. Finally, as will be discussed in more detail later, our results for firms tying

compensation to VBM systems are similar to those of Wallace (1997), who developed his

sample through a search of proxy statements.7

Ultimately, the possibility of some type of response bias cannot be totally ruled out, and

the results should be interpreted with this caveat. However, similar to Graham and Harvey

7 In a private discussion with an executive at a Fortune 500 firm, we learned that his company changed his bonus plan from an earning-based performance metric to EVA®. However, the description of the bonus plan in the proxy statement did not change and contained the same generic explanation of the bonus plan both before and after the adoption of the VBM metric. We verify from proxy statements that survey firms that indicate they base bonuses on VBM do have a bonus plan, but we generally cannot verify from the proxy statement that they use VBM. Using proxy statements to identify a sample omits most firms in the Ryan and Trahan (1999) survey that indicate that they use VBM systems for compensation and all firms that use VBM only for other purposes.

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(2001), we believe that these data represent the population and provide useful information

pertaining to a popular but relatively little-studied corporate control mechanism.

3.2 Method

Ideally, we would like to examine the direct relation between the adoption of a VBM

system and stock price appreciation. Since the market capitalizes expected future cash flows in

the firm’s stock price and firms can potentially use financial policy decisions to signal expected

improvements in advance of the public disclosure of operating results, it is difficult to construct

direct tests of the relation between the adoption of VBM systems and shareholder wealth.

Additionally, the decision to adopt a VBM system generally cannot be observed by investors,

and it is rarely possible to identify a specific event date. However, we can test the prediction that

adopting a VBM system should result in an increase in future cash flows net of the firm’s cost of

capital, i.e., residual income. Thus, we investigate the relation between a firm’s economic

performance, measured by residual income, and its adoption of a VBM system.8

To create financial value, a firm must generate cash flow in excess of that required to

cover the firm’s cost of capital, e.g., the firm must earn an economic profit. To measure

economic performance we use residual income as a proxy for economic profit earned by the

firm. Conceptually, the market value added by a firm (market value minus book value) is the

present value of expected future residual income discounted at the cost of capital. To estimate

8 We utilize residual income as a performance metric that is conceptually tied to market value. This approach is consistent with studies by Wallace (1997) and Hogan and Lewis (2005). Others point out complexities of the relation between residual income and value that are beyond the scope of this research. Tomkins (1975a), Amey (1975), Tomkins (1975b), and Emmanuel and Otley (1976) examine the controversy over the use of residual income as a tool for measuring the performance of managers of business segments. More recently, Ohlson (2003), and Pope and Wang (2003) examine the relation between positive NPV projects and the behavior of residual earnings. Cheng (2005) investigates the determinants of residual income.

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residual income, we follow Wallace (1997), but with enhancements to the tax rate estimates and

the cost of capital. We estimate residual income as follows (COMPUSTAT Data Item Numbers

are shown in parentheses):

Residual Income = Net Income + After-Tax Interest – Capital Charge

where

Net Income is before extraordinary items (A18);

After-Tax Interest is Interest Expense (A15) times (1-tax rate)

Capital Charge is Invested Capital (A37) times a cost of capital.

We use the statutory corporate tax rate in our calculations, which results in a tax rate of

46% before 1986, 40% in 1987, 34% from 1988 through 1993, and 35% after 1993. To estimate

the cost of capital, we also need estimates of the cost of equity (Re), the cost of debt (Rd), and the

weights of debt and equity (wd and we) in a firm’s capital structure. We calculate the beta for the

cost of equity and the cost of debt for each firm year, which results in a unique cost of capital

estimate for each firm in each year. We use the capital asset pricing model to estimate the cost of

equity and rate of return on ten-year treasury bonds as the risk-free rate. Based on Damodaran’s

(http://pages.stern.nyu.edu/~adamodar/) estimates from 1962 to1997, we use a 5.5% risk

premium. To estimate beta, we regress monthly returns on the CRSP value-weighted index for

the sixty months prior to the year of the estimate. To estimate the cost of debt, we first obtain

each firm’s long-term debt rating from COMPUSTAT. If the rating is unavailable, we use a

linear regression model to estimate a rating based on profitability, interest coverage, debt ratio,

size, and total asset turnover. We obtain corporate bond returns for Aaa and Baa bonds from the

Federal Reserve Board’s H.15 report, and extrapolate to estimate the return for other bond

ratings. We use the market value of the firms’ common stock and, in the absence of reliable

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estimates of market value, the book value of the firm’s long-term debt to impute weights. Using

these data, we compute the cost of capital (COC) as follows:

COC = wdRd(1-tax rate) + weRe.

We test the hypothesis that adopting a value-based management system impacts firm

performance in the post-adoption period relative to the pre-adoption period. We define the year

that a VBM system is adopted as year 0 and compare residual income for the year preceding the

adoption year to five years following the adoption year (–1 to +1, –1 to +2, –1 to +3, –1 to +4,

and –1 to +5). Going out for five years post-adoption allow us to examine both short-and long-

run effects. Specifically, defining Mt as the median of a particular performance metric, we test

the following hypothesis:

H0: Mi = Mj, i = –1, +1 to +5, j = –1

H1: Mi ≠ Mj, i = –1, +1 to +5, j = –1.

To control for economy-wide and industry effects that can induce mean reversion in

operating performance, we follow Barber and Lyon (1996). We first attempt to match based on

industry (2-digit SIC codes), economic performance (+/– 10 percent of residual income as a

percentage of invested capital), and size (+/–30% of assets). If no match is found based on these

criteria, we attempt to match based on industry (1-digit SIC codes), economic performance (+/–

10 percent of residual income as a percentage of invested capital), and size (+/–30% of assets).

If a match is still not found, we match on economic performance (+/– 10 percent of residual

income as a percentage of invested capital) and industry (first 2-digit and then 1-digit SIC code)

with the closest size match as possible. As prescribed by Barber and Lyon, if we are still unable

to get a matching firm we match based on performance (+/– 10 percent of residual income as a

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percentage of invested capital). We are able to match by industry (at least at the 1-digit level)

and performance for all but 1 of the 84 observations. We exceed the +/- 30% size criterion in 18

cases (21% of the sample). As a robustness check, we conduct our tests on the set of 66 firms

matched within size criterion and get similar results to those reported for the full sample.

We next compute the matched-firm-adjusted changes in residual income for the five post-

adoption periods (–1 to +1, –1 to +2, –1 to +3, –1 to +4 and –1 to +5). Results for year 0 are

excluded since they potentially reflect both pre- and post-adoption incentives and do not provide

time for operating changes to affect performance. We define the matched-firm-adjusted changes

as the change in the variable minus the corresponding change in the variable for the matching

firms over the relevant period.

We measure change in residual income in five ways: (1) in levels (2) as a percentage

change, (3) as a fraction of annual sales (4) as a fraction of end-of-period total assets, and (5) as a

fraction of end-of-period invested capital. We standardize by a proxy for size to control for the

possibility that the sample firms may pursue different growth, acquisition, or divestiture

strategies than do the control firms after the adoption of a value-based management system. The

data are skewed and contain extreme values. To control for outliers that can inordinately

influence results in small samples, we use a two-tailed non-parametric signed-rank test to test for

paired differences in the medians of the test firms and the control firms.

Table 2 provides summary statistics for the 84 sample firms that have adopted value-

based management systems from 1984-1997. The median sample firm has assets of $1.3 billion,

sales of nearly $1.44 billion, and invested capital of $640 million in the year prior to adoption.

The mean (median) operating profit equals $384.148 million ($137.478 million). The average

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(median) cost of capital is 11.74% (11.33%). The minimum cost of capital estimated is equal to

7.62% and the maximum is equal to 19.45%. The mean (median) residual income equals

$140.689 million ($59.690 million). On average, residual income equals 16.375% of invested

capital. Thus, the typical firm that adopts a VBM system is profitable and recovers its cost of

capital prior to adoption. Since Barber and Lyon (1996) find that matching by prior performance

is the most important characteristic to improve the power of operating performance tests, we also

present adjusted residual income. We note that neither the mean nor the median adjusted

residual income significantly differ from zero for the non-standardized value or any of the size-

standardized estimates.

We also find that over 76% of the firms adopt VBM coincident with a restructuring

program. Two-thirds of the firms use the VBM metric as a component of its compensation

system. The typical firm has 5.4% managerial ownership, but the ownership data are skewed as

evidenced by the average value of 12.99%. Capital expenditures average 9.44% of sales, and net

working capital comprises 17.89% of assets on average. The mean (median) market-to-book

asset ration is 1.80 (1.49).

3.3. Endogeneity and Sample Selection Bias

We want to examine if the adoption of a value-based management system impacts the

future economic performance of the firm. To accomplish our objective, we examine the

matched-firm-adjusted changes in residual income for adopting firms for intervals subsequent to

adoption. However, we recognize that endogenous relations between economic performance and

the adoption of VBM systems for compensation purposes possibly create sample biases that

could lead to faulty conclusions. For instance, poorly performing firms that face tougher

15

obstacles to improving performance might tie compensation to the VBM metric to provide

managers with additional incentives to improve. Alternatively, managers could opportunistically

influence bonus targets to be too low or adopt VBM systems to take advantage of predictable

changes in performance; i.e., anticipated performance improvement results in the adoption of a

VBM system. If so, the gains might have little to do with the VBM incentives. Although

matching on firm performance should mitigate this concern to some degree, the matching

process cannot completely eliminate the possibility.

To control for these potential biases, we use a two-stage selection model for treatment

effects, the adoption of a VBM system in this case (see Heckman, 1979; Greene, 1981; Barnow,

et al., 1981). In the first stage, we estimate a probit model to explain why the firm ties

compensation to the VBM system. This model is discussed in greater detail in Section 4.2.1. In

the second stage, we use the inverse Mills ratio from this probit model as a control variable in

our regression to control for any possible selection bias. We also adjust for the correlation

between the residuals from the probit model and the residuals in the second-stage regression to

produce a consistent estimate of standard errors.

4. Results

In this section, we present univariate and multivariate results from our analysis of the use

and efficacy of VBM systems. Our results provide insights into (1) whether VBM improves

firm performance and (2) firm characteristics that enhance or hinder the effectiveness of these

systems.

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4.1. Univariate Results

To examine the economic efficacy of VBM systems, we compare residual income for

periods pre- and post-adoption of a VBM system. In addition to the basic relation between VBM

adoption and residual income, we also examine effectiveness based on whether the firm ties

compensation to the VBM system, growth opportunities, the pre-adoption level of capital

expenditures, and the pre-adoption level of net working capital. As robustness checks, we also

control for corporate restructuring, insider ownership, and timing of adoption.

4.1.1. VBM and Residual Income

Table 3 compares residual income for the periods pre- and post-adoption of a value-based

management system. We show the results for the five windows relative to the year of adoption

(year 0) of a value-based management system. Results are shown for all 84 firms in the sample

and for the 72 firms that remain available for all years. As a robustness check, we present the

results for those firms that match within 30% on size. The results measure median changes in

residual income relative to a matched firm. We report dollar changes, percentage changes, and

changes standardized by invested capital, assets, and sales.

The results in Table 3 support the premise that VBM improves economic performance.

The median change in relative residual income divided by invested capital is 4.52, 4.95, 4.46,

8.96, and 7.55 for periods –1 to +1, –1 to +2, and –1 to +3, –1 to +4, and –1 to +5 respectively.

These changes are statistically significant (p-values of 0.00 for four periods and 0.03 for one

period). We obtain similar results for the other residual income measures, which we present for

completeness. The results are statistically significant at conventional levels for all measures, for

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all samples, in all post-adoption periods, except for residual income in dollars in periods –1 to +2

and –1 to +3.

In sum, the results support a positive relation between the adoption of a value-based

management system and increases in future economic performance, measured by residual

income. Since the results are consistent across several measures, we will present subsequent

results only for residual income divided by invested capital.

Residual income is affected by after-tax operating profit, invested capital, and the cost of

capital. To examine whether any one component of residual income drives the post-adoption

performance improvement, we examine median changes in each of the three components relative

to matched firms for each of the five post-adoption periods. The results, shown in Table 4, show

that no one component of residual income drives the results, but rather, all three components

appear to work together. Operating profit (cost of capital) shows steady increases (decreases)

throughout the post-adoption period. In contrast, invested capital has a small relative decrease in

year t+1 and large decreases in years t+2 through t+4. There is a negligible decrease through

year t+5, indicating that invested capital returns to the pre-adoption level. A possible

explanation for this pattern is that VBM encourages more efficient use of invested capital, but

that reductions occur over time as firms modify their processes. As the adopting firms grow

sales and operating performance, we conjecture that they then need additional invested capital to

support their operations. Supporting this conjecture, we find (in results not reported in a table)

that, relative to the matched firm, sales increase significantly in year t+5 for the adopting firm,

but invested capital as a percentage of sales remains relatively constant.

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4.1.2. Compensation and VBM Effectiveness

A comprehensive VBM system typically ties executive compensation to the VBM metric,

usually with a cash bonus. A strong relation between compensation and the VBM metric should

align managers’ interests with those of shareholders and provide them with the incentives to

improve economic performance. To examine the influence of tying compensation to the VBM

system, we classify firms as to whether or not compensation is tied to the VBM metric, as

reported by responding companies. We present these results in Table 5.

The median firm that ties compensation to VBM improves residual income for all post-

adoption intervals. All of the changes are statistically significant except for –1 to +3 in the 72-

firm sample. Firms that do not base compensation on the VBM metric also experience positive

changes for all post-adoption intervals, which are statistically significant for all intervals. While

the improvements are larger for non-compensation firms except for period –1 to +5, none of the

differences are statistically significant except for period –1 to +3 for the 72-firm sample. Thus,

despite the proposed incentive alignment, firms that base compensation on VBM do not appear

to improve economic performance better than those firms that do not use VBM for

compensation. This result is consistent with Riceman, et al., (2002) One possible explanation,

mentioned above, is that firms that tie compensation to VBM face tougher obstacles to

improving economic performance and use the VBM metric to provide managers with greater

incentives. We will explore this possibility in our multivariate model, which includes controls

for sample bias.

19

4.1.3 Growth Opportunity and VBM Effectiveness

High-growth firms derive a greater proportion of their value from future investments as

opposed to assets in place (Myers, 1977). Performance improvements associated with VBM

could be higher for growth firms if the system helps these firms identify those portions of their

investment opportunities that can be converted into cash flows. Alternatively, greater

information asymmetries characterize high-growth firms, which make it more difficult to design

corporate governance systems for these firms. Holmstrom (1979) argues that contracts based on

observable outcomes (e.g., stock price appreciation) result in a second-best solution. He

suggests that contracts can be improved if firms obtain additional information about the

managers’ actions. If VBM generates information about which actions create value, then VBM

could be more effective for high-growth firms.

To examine the relation between growth opportunities and VBM effectiveness, we

classify firms based on the market-to-book ratio of assets. We define high-growth firms as firms

with market-to-book assets above the sample median in the year prior to VBM adoption, and

low-growth firms as those below the sample median. For our sample, the median market to book

asset ratio is 1.49. Table 6 presents the analysis of residual income segmented by growth

opportunity.9 Our findings suggest that VBM is no more or less effective for the high-growth

firms than for the low-growth firms. Both groups of firms experience positive changes in

relative residual income divided by invested capital for all post-adoption periods. The changes

9 The matching firms have higher mean and median market-to-book ratios in paired tests. Since the market-to-book ratio measures market expectations of future growth opportunities, the market expects higher growth in economic profit for the matched firms. Thus this difference in growth opportunity strengthens the basic finding that VBM firms improve economic profit relative to the matched firm. The matched firms have larger market-to-book ratios in both the high- and the low-growth categories so we do not believe that it affects our comparison by growth opportunity. However, the reader should be aware of this difference when interpreting the results.

20

are statistically significant for all but period –1 to +2 for the 72-firm sample. None of the

differences between high-and low-growth firms are statistically significant.

4.1.4. Capital Investment and VBM Effectiveness

In Table 5, we present an analysis of VBM efficacy segregated by capital investment

intensity. Value-based management systems reward managers for the efficient allocation of

invested capital and penalize managers for overinvestment. This feature suggests that VBM

could be more useful to those firms with higher capital expenditures. We classify firms with

capital expenditures above the sample median as high-capital-expenditure firms and firms with

capital expenditures below the sample median as low-capital-expenditure firms.

Both groups of firms experience positive changes in relative residual income divided by

invested capital for all post-adoption periods. All changes are statistically significant. However,

the differences between high-and low-capital expenditure firms are not statistically significant

except for period -1 to +5 for the 72-firm sample.

4.1.5. Working Capital and VBM Effectiveness

Table 5 also presents results based on a firm’s investment in net working capital. Firms

that are working capital intensive potentially derive a large percentage of their value from short-

term assets in place, which can be observed and monitored. However, working capital is a non-

budgetary item (e.g., it is not formally analyzed as are capital projects) that is often influenced by

discretionary decisions. Since reducing the investment in working capital improves the VBM

performance metrics by reducing invested capital, the benefits of adopting a VBM system may

be greater for firms with high levels of working capital. In contrast, Holmstrom’s (1979)

21

arguments suggest that VBM could be more useful to low-net-working-capital firms since these

firms have fewer observable assets and VBM would generate additional information about

managers’ actions.

To examine the relation between working capital and VBM effectiveness, we classify

firms as having either high net working capital or low net working capital. High-net-working-

capital firms have a level of net working capital as a percentage of assets that is above the sample

median, and low-net-working-capital firms have a level of net working capital as a percentage of

assets below the sample median. We measure net working capital as current assets minus current

liabilities divided by total assets in the year prior to adoption of a VBM system.

The results for high-net-working-capital and low-net-working-capital firms are not

markedly different. High-net-working-capital firms experience a statistically significant

improvement in changes in relative residual income divided by invested capital for all post-

adoption periods. Low-net-working-capital firms also experience an improvement in all post-

adoption periods, but the results are not statistically significant in the -1 to +2 and -1 to +3

periods. The differences between high- and low-net-working-capital firms are not statistically

significant for any of the periods.

4.1.6. Univariate Robustness Checks

Many firms restructured their operations during the period of our study (1984–1997).

Practitioners indicate in private conversations that firms often adopt VBM systems in

conjunction with corporate restructuring activities and use the VBM metrics to provide

employees with the incentives to implement the restructuring as well as to measure and

consolidate gains. This practice raises the possibility that we could attribute gains from

22

restructuring to the VBM system. We search news wires on the Academic Universe Lexis/Nexis

system and classify firms as restructuring if they have undergone operational restructuring,

reorganization, reengineering, divestitures, or spin-offs during the period from three years prior

to adopting a value-based management system through two years after adoption. In results not

reported in a table, we do not find any significant differences based on whether a firm

restructures prior to or in conjunction with the adoption of VBM.

Stock ownership by insiders could confound our results. Managerial ownership possibly

substitutes for a VBM system and serves as an alternate mechanism for aligning manager and

shareholder interests, or it could complement the VBM system and provide managers with

greater incentives to make the VBM implementation successful. We gather ownership data from

proxy statements when available and from the Value Line Investment Survey if the proxy is not

available. To examine the influence of managerial ownership, we classify firms based on

whether the level of managerial ownership as a percentage of shares outstanding is high (above

the sample median) or low (below the sample median). We do not find any significant

differences in post-adoption performance based on the level of managerial stock ownership.

Value-based management techniques began to receive considerable coverage by the

business media in the early 1990s, which lead to an increase in the popularity of these systems

among businesses. The adoption patterns in Table 1 indicate that 47 of the 84 adoptions in our

sample occurred during the ten-year period from 1984-1993, and that another 47 adoptions

occurred during the four-year period of 1994-1997. Possibly, firms who adopt in the later period

could nominally embrace VBM as a form of “lip service” to value creation with little or no

commitment to the program. To analyze a potential influence, we divide our sample into firms

that adopt before 1994 and those that adopt after 1994. We observe a similar pattern of

23

improvement to that for the overall sample in both subsamples. We also find that the

improvement in performance does not differ across these two subsamples. Thus, the data do not

indicate a difference in efficacy based on the timing of adoption.

4.2. Multivariate Results

4.2.1. Probit Analysis of the Decision to Use Value-based Management for Compensation

To gain insight into why firms adopt VBM systems, we use a probit model to estimate

the likelihood that a firm that adopts VBM ties compensation to the VBM metric. This probit

model is then used as the first stage in a two-stage model to control for possible selection bias.

Based on our previous discussions, we model the choice to base compensation on the VBM

metric as a function of prior performance (adjusted residual income divided by invested capital

in the year prior to adoption), concurrent restructuring, firm size, inside ownership, industry-

adjusted capital expenditure intensity, growth opportunity, and the percentage of assets invested

in net working capital. Since firms might be more likely to tie compensation to VBM if other

firms in their industry have also adopted VBM, we include a dummy variable that indicates if at

least one other sample firm in the same two-digit SIC code industry has tied compensation to a

VBM system within the previous three years. Table 7 presents the results.

Despite matching within ten percent based on prior residual income, we find that firms

with positive matched-firm-adjusted prior performance are more likely to tie compensation to the

VBM metric (p-value=0.00). Firms with higher growth opportunities, measured by the market-

to-book ratio, are less likely to tie compensation to VBM (p-value = 0.00). The percentage of

assets invested in net working capital (p-value = 0.04) is negatively related to the use of VBM

24

for compensation, and the use of VBM for compensation by other sample firms in the industry

(p-value = 0.09) is positively related to the use of VBM for compensation.

The positive relation with prior performance may indicate that firms characterized by

good performance strongly focus on value creation and tie compensation to VBM to provide

managers with additional value-based incentives. It is also consistent with the notion that

managers may “game” the system by adopting VBM systems tied to compensation in

anticipation of continuing strong performance. The negative relation with the market-to-book

asset ratio is consistent with the premise that equity-based compensation is more efficient for

high-growth firms. High-growth firms, which are more difficult to monitor since they possess

fewer tangible assets, provide their executives with more equity-based compensation than do

low-growth firms (e.g., Smith and Watts, 1992; Gaver and Gaver, 1993). Ryan and Wiggins

(2001), confirm a positive relation between stock options and the market-to-book ratio of assets,

but find no relation between cash bonus and the market-to-book ratio.

Since firms with low levels of working capital are likely to have higher levels of

unobservable assets that are more difficult to monitor, the negative relation with net working

capital is consistent with the premise that firms with more unobservable assets benefit the most

from tying compensation to VBM. Firms are also more likely to tie compensation to VBM when

at least one other firm in the industry has done so. This finding suggests some degree of industry

herding. This herding could arise from industry characteristics conducive to the use of VBM for

compensation, competition in the managerial labor market, mimicry, or increased comfort with

the system if a competitor validates its use.

25

4.2.2. Multivariate Regression Analysis of Residual Income

To simultaneously control for firm-specific factors that may impact VBM effectiveness,

we use a multivariate OLS regression model to examine the relation between VBM adoption and

economic performance for the post-adoption periods under consideration. As noted previously,

the data is skewed and contains extreme values, which complicates the use of least squares

regressions. Thus, to mitigate the influence of outliers, we winsorize the residual income

estimates at the 5 percent and 95 percent levels. For the sake of robustness, we also estimate our

regressions on the raw data and on a trimmed sample that deletes extreme values. The

qualitative results are similar in all cases, although, as expected if the sample is influenced by

extreme values, we lose some statistical significance and explanatory power when we use the

raw sample. Also, as noted above, we include the inverse Mills ratio from the probit estimation

and adjust the estimate of the standard errors to control for possible selection bias.

We use residual income divided by invested capital as our dependent variable. We

include a dummy variable for restructuring, a dummy variable for compensation, the percentage

of inside ownership, industry-adjusted capital expenditures divided by sales, the market-to-book

asset ratio, net working capital divided by assets, the log of total assets, and the inverse Mills

ratio from the probit as independent variables. We present the results of our regressions for the

five post-adoption years in Table 8.

The strongest result is that large firms appear to be less successful than small firms in

achieving benefits from adopting value-based management systems. The coefficients for size are

negative and statistically significant for all five post-adoption years. This is consistent with

larger firms facing more complex problems and more difficult implementations. We find

26

positive relations for years 1 and 2 significant at a 0.03 and 0.06 levels respectively, between

post-adoption performance and compensation plans based on VBM. There is some weaker but

inconsistent evidence of VBM effectiveness being related to some of the other variables.

Looking at the 72-firm sample, “restructuring” is positive and significant for years 2 and 3, “used

for compensation” is negative and significant for years 1 and 2, inside ownership is positive and

significant for year 3, and working capital intensity is negative and significant for year 1.

In our probit regression, we find that firms that over perform in the pre-adoption period

are more likely to tie compensation to VBM. Evidence of a selection bias is indicated by the

significant coefficient for the inverse Mills ratio in our multivariate regression, for years 1 and 2.

After controlling for this bias, the coefficient for the compensation dummy variable is negative

and significant in these years, indicating that tying compensation to VBM is negatively related to

future performance improvements. Again, this result is significant for the first two post-adoption

years. This result raises the possibility that some firms may adopt VBM systems tied to

compensation in order to benefit from anticipated future strong performance. If so, management

could influence the firms to set performance targets too low, which results in weaker

improvements. Alternatively, one could interpret the data to suggest that good firms are more

likely to tie compensation to VBM, and that these firms have less potential for improvement on

the margin. Overall, the adjusted R2s are low for these regressions, and we do not draw strong

conclusions from these results.

Our sample consists of systems based on discounted cash flow, cash flow return on

investment, return on invested capital, residual income, and a few customized systems that we

designate as “other.” All of these systems include estimates of cash flow or income, invested

capital, and some consideration of the cost of capital. A natural question arises: Does one

27

system result in greater improvement than another? If so, generalizations based on a mixed

sample could be misleading. As a robustness check, we estimate our regressions in Table 7 with

indicator variables for each type of VBM system. We do not find any consistent pattern to

indicate that one system dominates another.

4.3. Analysis of Capital Investment

Our evidence, reported in Table 6, suggests that VBM is effective in both high- and low-

growth firms. However VBM metrics focus on the current period and provide incentives to

reduce invested capital. This combination leads to a natural question: Does the increase in short-

term residual income in high-growth firms come at the expense of capital investment? If so, any

gains from a myopic investment strategy may come at the expense of shareholder value.

To shed light on this issue, we examine the relation between VBM adoption and the

change in industry-adjusted capital expenditures as a percentage of sales for both high-capital-

expenditure firms and high-growth firms. If, as conjectured, VBM provides incentives to

mitigate the overinvestment problem, we expect that firms that invest more than their matching

firm will cut capital expenditures (high-capital-expenditure firms) following the VBM adoption.

If the single-period nature of the VBM metric induces an unintended short-term focus we expect

high-growth firms to cut industry-adjusted spending following the adoption of a VBM system.

We present the results from our analysis in Table 9.

In general, firms reduce capital expenditures subsequent to VBM adoption, with the

median changes being negative and significant for all post-adoption periods except –1 to +1.

The median changes in capital expenditures to sales relative to the industry medians are 0.07 (p-

value = 0.28), –0.65 (p-value = 0.02), –0.74 (p-value = 0.00), –1.19 (p-value = 0.00), and –1.32

28

(p-value = 0.00). These results are driven by the high-capital-expenditure firms. The median

changes for high-capital-expenditure firms are –0.85 (p-value = 0.11), –1.44 (p-value = 0.00), -

1.49 (p-value = 0.01), –1.98 (p-value = 0.01), and –3.73 (p-value = 0.00), while the low-capital-

expenditure firms only show a statistically significant change in capital expenditures for the –1

to +5 period, -0.79 (p-value = 0.01). The differences between high- and low-capital-expenditure

firms are statistically significant for all of the post-adoption periods except for –1 to +1 (p-value

= 0.12). Overall, we conclude that high-capital-expenditure firms are more likely to cut capital

expenditures subsequent to adopting a VBM system.

We do not find evidence that VBM encourages underinvestment in high-growth firms,

which suggests that improvement in residual income does not come at the expense of long-term

value. As shown in Table 9, the changes in capital expenditures are not statistically different for

high- or low-growth firms. High-growth firms are no more likely to cut expenditures in the post-

adoption period than are low-growth firms. Thus, we find no support for the premise that the

single-period metric and focus on invested capital results in short-term improvements at the

expense of long-term investment. Instead, the data support the premise that VBM encourages

efficient levels of invested capital.

5. Summary and Conclusion

Effective corporate governance and financial control includes the use of monitoring and

incentive mechanisms to encourage the creation of shareholder value. Value-based management

systems attempt to accomplish this goal by providing managers with a set of decision-making

tools that identify which alternatives create or destroy value, and often link compensation and

promotions to metrics associated with shareholder value. Although the use of VBM has

29

increased significantly in past years, we have only limited information on its efficacy as a means

of corporate governance. To shed light on this issue, we investigate two related research

questions: (1) Does the adoption of a VBM system improve operating performance? and (2)

What factors enhance or hinder the effectiveness of VBM systems?

We find that the typical firm in our sample significantly improves residual income

following the adoption of VBM. No one component of residual income (after-tax operating

profit, invested capital, or cost of capital) drives the result, but rather all three components appear

to work together. VBM does not appear to be more or less effective when used for compensation

or when adopted by firms with high or low levels of capital expenditures, growth opportunities,

or investment in working capital. We do not find any indication that gains in residual income

come at the expense of long-term investment.

Firms are more likely to tie compensation to VBM when they perform better prior to

adoption and when other firms in the industry have also tied VBM to compensation. Firms with

higher growth opportunities are less likely to tie VBM to compensation, consistent with options

being more efficient for these firms. Firms with higher levels of working capital are also less

likely to tie VBM to compensation. Controlling for these characteristics, we find that

performance subsequent to VBM adoption is negatively related to firm size. We also find that

performance improvements relate negatively to the use of VBM for compensation in the first two

years. However, the adjusted R2s for these regressions are low. Although consultants often tout

the benefits of their particular VBM performance metrics, we do not find evidence that one

metric is better than others. Finally, we find that firms reduce capital expenditures following

VBM adoption. These reductions in spending do not differ based on the firms’ growth

30

opportunities, which suggest that the improvement in performance does not come at the expense

of long-term value.

In sum, our analysis suggests that VBM produces improvements in economic

performance. Overall, matched-firm-adjusted residual income is higher in all five post-adoption

years relative to the year prior to adoption. The use of value-based management systems is

associated with improved economic performance for a sustained period.

31

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Table 1 Distribution by year of 84 value-based management systems adopted in the period 1984-1997.

Year of adoption

Discounted Cash Flow

Cash Flow Return on Investment

Return on Invested Capital

Residual Income

Other Value-based Metrics Total

1984 1 0 0 0 0 1 (1.23%)

1985 1 0 1 1 0 3 (3.57%)

1986 2 0 0 0 0 2 (2.38%)

1987 0 0 0 0 0 0 (0%)

1988 5 1 1 0 0 7 (8.33%)

1989 1 0 1 1 0 3 (3.57%)

1990a 0 1 1 0 0 1 (1.23%)

1991a 1 1 2 0 0 3 (3.57%)

1992 1 2 0 4 0 7 (8.33%)

1993a 2 2 1 6 0 10 (11.90%)

1994a 1 0 0 10 1 11 (13.10%)

1995a 1 2 0 12 3 17 (20.24%)

1996a 4 0 3 11 1 18 (21.43%)

1997 0 0 0 1 0 1 (1.23%)b

Total 20 (23.81%) 9 (10.71%) 10 (11.90%) 46 (54.76%) 5 (5.95%) 84 (100%) a Some firms adopted systems using multiple metrics resulting in a total of 90 metrics adopted in 84 value-based management adoptions. The number of metrics adopted in these years and the total number of metrics adopted will add to greater than the total number of value-based management adoptions. b The survey was mailed in November of 1996, with second requests sent in February of 1997. Therefore, only a partial year of adoptions is included for 1997.

Table 2 Summary statistics for in the year before adoption for 84 firms adopting a value-based management system in 1984-1997 (dollars in millions). We present mean and median firm and matched-firm-adjusted statistics for size, residual income, residual income as a percent of assets, and residual income as a percent of sales. We base matched firms on performance, industry, and size per Barber and Lyon (1996). Residual income equals net income before extraordinary items plus after-tax interest less a capital charge. We base statistical tests on t-tests for means and on non-parametric signed rank tests for medians.

Non-standardized Percent of invested capital (%) Percent of assets (%) Percent of sales (%) Mean Median Mean Median Mean Median Mean Median

Assets ($ millions) 4,508.712 1,300.901

Sales ($ millions) 3,981.157 1,439.029

Invested capital ($ millions) 2,472.394 639.987

Operating Profit ($ millions) 384.148 137.478

Cost of capital 11.74 % 11.33%

Residual income ($ millions) 140.689 59.690** 16.3705** 10.1328*** 5.8654*** 5.9153*** 5.2973*** 5.8126***

Adjusted residual income ($ millions) 27.518 6.505 -0.0676 -0.0207 -1.5435 -0.0619 -0.8547 -0.4198

Prior or coincident restructuring 76.19%

Uses VBM for compensation 66.67%

Managerial ownership 12.99% 5.40%

Capital expenditures/sales 9.44% 6.26%

Net working capital/assets 17.89% 16.57%

Market-to-book assets 1.80 1.49

*** Significant from zero at the 0.01 level ** Significant from zero at the 0.05 level * Significant from zero at the 0.10 level

Table 3 Effects of the adoption of value-based management on residual income. We present the median change in residual income relative to a matched firm for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. Following Barber and Lyon (1996), we match within 10% of prior performance (residual income divided by invested capital, 30% of size, and within the two- or one-digit SIC code when possible. If we cannot match on all metrics, performance takes priority. As a robustness check, we also present the results for those firms that match within 30% by size. Residual income equals net income before extraordinary items plus after-tax interest less a capital charge. We base p-values (in parentheses) on non-parametric signed rank tests. Change from year i to year j t-1 to t+1 t-1 to t+2 t-1 to t+3 t-1 to t+4 t-1 to t+5

Entire Sample N=84 N=84 N=84 N=79 N=72

Residual Income Divided by Invested Capital (%) 4.5202 (0.00)

4.9489 (0.00)

4.4556 (0.03)

8.9588 (0.00)

7.5507 (0.00)

Residual Income as a Percentage of Assets (%) 2.2489 (0.00)

3.2055 (0.00)

2.7022 (0.03)

5.9955 (0.00)

4.2226 (0.00)

Residual Income Divided by Sales (%) 2.3563 (0.00)

2.2985 (0.00)

2.6020 (0.00)

4.5163 (0.00)

3.0494 (0.00)

Percentage Change in Residual Income (%) 62.1448 (0.00)

78.5721 (0.03)

97.2853 (0.01)

179.484 (0.01)

197.7736 (0.00)

Residual Income ($ millions) 20.0418 (0.00)

15.0909 (0.33)

26.8866 (0.14)

60.0225 (0.02)

67.6731 (0.00)

Sample of 72 Firms Available in All Years

Residual Income Divided by Invested Capital (%) 4.4292 (0.00)

5.0835 (0.00)

4.3119 (0.03)

8.9987 (0.00)

7.5507 (0.00)

Residual Income as a Percentage of Assets (%) 2.5531 (0.00)

3.5575 (0.00)

3.1423 (0.03)

5.4466 (0.00)

4.2226 (0.00)

Residual Income Divided by Sales (%) 2.9909 (0.00)

2.4957 (0.00)

2.7593 (0.00)

4.1781 (0.00)

3.0494 (0.00)

Percentage Change in Residual Income (%) 77.2250 (0.00)

86.3121 (0.02)

97.2853 (0.02)

155.0320 (0.01)

197.7736 (0.00)

Residual Income ($ millions) 24.8398 (0.00)

22.1010 (0.22)

26.8866 (0.19)

54.5567 (0.03)

67.6731 (0.00)

Matched Firm Within 30% by Size N=66 N=66 N=66 N=62 N=55 Residual Income Divided by Invested Capital (%) (matched firm within 30% by size)

4.6816 (0.00)

5.6934 (0.00)

4.5775 (0.00)

8.7836 (0.00)

7.8891 (0.00)

Residual Income as a Percentage of Assets (%) 2.5531 (0.00)

3.1614 (0.00)

2.6256 (0.00)

5.0019 (0.00)

5.5141 (0.00)

Residual Income Divided by Sales (%) 2.3563 (0.00)

2.0156 (0.00)

2.6020 (0.00)

4.8199 (0.00)

3.9308 (0.00)

Percentage Change in Residual Income (%) 71.5927 (0.00)

81.7867 (0.08)

108.5064 (0.05)

168.4268 (0.05)

234.7891 (0.03)

Residual Income ($ millions) 20.0481 (0.00)

15.0909 (0.31)

26.8866 (0.19)

65.9744 (0.04)

80.7706 (0.01)

Table 4 Effects of the adoption of value-based management on the components of residual income. We present the median change in after-tax operating profit, invested capital, and cost of capital relative to a matched firm for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. Following Barber and Lyon (1996), we match within 10% of prior performance (residual income divided by invested capital, 30% of size, and within the two- or one digit SIC code when possible. If we cannot match on all metrics, performance takes priority. We base p-values (in parentheses) on non-parametric signed rank tests.

Change from year i to year j t-1 to t+1 t-1 to t+2 t-1 to t+3 t-1 to t+4 t-1 to t+5

Entire Sample N=84 N=84 N=84 N=79 N=72

After-tax Operating Profit ($ millions) 7.3249 (0.36)

10.9406 (0.79)

18.9576 (0.59)

66.8825 (0.06)

63.7044 (0.12)

Invested Capital ($ millions) -4.7075 (0.22)

-67.5615 (0.02)

-69.5110 (0.04)

-65.0000 (0.09)

-0.4815 (0.23)

Cost of Capital (%) -0.1988 (0.52)

-0.2038 (0.97)

-0.2610 (0.64)

-0.4855 (0.16)

-0.2452 (0.38)

Sample of 72 Firms Available in All Years

After-tax Operating Profit ($ millions) 25.0128 (0.08)

19.5894 (0.69)

37.4833 (0.31)

55.1963 (0.09)

63.7044 (0.12)

Invested Capital ($ millions) 5.9850 (0.70)

-14.7695 (0.22)

-20.6030 (0.25)

-45.1095 (0.25)

-0.4815 (0.23)

Cost of Capital (%) -0.0199 (0.50)

-0.2005 (0.99)

-0.4198 (0.47)

-0.6114 (0.09)

-0.2452 (0.38)

Table 5 Effects of the adoption of value-based management on residual income by use for compensation. We present the median change in residual income as a percentage of invested capital relative to a matched firm for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. Following Barber and Lyon (1996), we match within 10% of prior performance (residual income divided by invested capital, 30% of size, and within the two- or one digit SIC code when possible. If we cannot match on all metrics, performance takes priority. Residual income equals net income before extraordinary items plus after-tax interest less a capital charge. We base p-values (in parentheses) on non-parametric signed rank tests.

Change from year i to year j t-1 to t+1 t-1 to t+2 t-1 to t+3 t-1 to t+4 t-1 to t+5

Entire Sample

Firms that base compensation on VBM 4.4293 (0.00)

[N=56]

3.7855 (0.01)

[N=56]

3.7821 (0.05)

[N=56]

8.8352 (0.00)

[N=53]

7.5507 (0.00)

[N=48]

Firms that do not base compensation on VBM 5.3231 (0.02) N=28

7.1778 (0.01) N=28

6.0630 (0.00) N=28

9.7983 (0.00) N=26

6.6489 (0.02) N=24

Wilcoxon p-value for Difference 0.79 0.54 0.26 0.64 0.82

Sample of 72 Firms Available in All Years

Firms that base compensation on VBM 4.3248 (0.01)

[N=48]

3.7855 (0.06)

[N=48]

3.3918 (0.23) [N=48]

8.2716 (0.00)

[N=48]

7.5507 (0.00)

[N=48]

Firms that do not base compensation on VBM 5.3231 (0.06) N=24

7.3520 (0.01) N=24

7.3702 (0.00) N=24

11.1469 (0.00) N=24

6.6489 (0.02) N=24

Wilcoxon p-value for Difference 0.84 0.27 0.08 0.31 0.82

Table 6 Effects of the adoption of value-based management on residual income by firm characteristics. We present the median change in residual income as a percentage of invested capital relative to a matched firm for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. Following Barber and Lyon, we match within 10% of prior performance (residual income divided by invested capital, 30% of size, and within the two- or one digit SIC code when possible. If we cannot match on all metrics, prior performance takes priority. We classify a firm as a high-capital-expenditure firm if the capital expenditures divided by sales exceeds the sample median. High-growth (high-net-working-capital) firms have market-to-book-asset (net-working-capital-to-asset) ratios above the sample median. Residual income equals net income before extraordinary items plus after-tax interest less a capital charge. We base p-values (in parentheses) on non-parametric signed rank tests and test for differences between classifications using two-sample Wilcoxon tests. Change from year i to year j t-1 to t+1 t-1 to t+2 t-1 to t+3 t-1 to t+4 t-1 to t+5

Entire Sample

High-growth Firms 3.7762 (0.04) N=42

6.3160 (0.03) N=42

5.0426 (0.00) N=42

5.2149 (0.01) N=39

6.2426 (0.06) N=34

Low-growth Firms 5.1917 (0.00) N=42

4.8398 (0.01) N=42

3.6163 (0.06) N=42

10.4981 (0.00) N=40

7.9570 (0.00) N=38

High-Capital-Expenditure Firms 4.6815 (0.02) N=42

4.3844 (0.03) N=42

4.9666 (0.01) N=42

9.1729 (0.01) N=38

9.8033 (0.06) N=34

Low-Capital Expenditure Firms 4.4752 (0.00) N=42

5.0835 (0.01) N=42

3.6740 (0.03) N=42

8.9588 (0.00) N=41

6.4697 (0.03) N=39

High-Net-Working-Capital Firms 4.4753 (0.01) N=42

5.3762 (0.00) N=42

4.1052 (0.00) N=42

8.7218 (0.00) N=40

6.1900 (0.01) N=37

Low-Net-Working-Capital Firms 5.0304 (0.01) N=42

4.3425 (0.17) N=42

4.801 (0.20) N=42

10.8899 (0.00) N=39

8.5870 (0.01) N=35

Sample of 72 Firms Available in All Years

High-growth Firms (N=36) 3.7762 (0.05)

5.2416 (0.14)

4.0408 (0.04)

4.5660 (0.00)

6.2426 (0.00)

Low-growth Firms (N=36) 5.0304 (0.00)

5.0835 (0.01)

4.3119 (0.04)

11.7668 (0.00)

8.0636 (0.00)

High-Capital-Expenditure Firms (N=36) 4.4293 (0.02)

5.55589 (0.06)

4.7484 (0.03)

9.1729 (0.01)

11.0165* (0.00)

Low-Capital-Expenditure Firms (N=36) 4.4753 (0.01)

5.0835 (0.03)

3.4495 (0.06)

8.9987 (0.00)

4.9155 (0.09)

High-Net-Working-Capital Firms (N=36) 4.4753 (0.01)

5.9103 (0.00)

3.6740 (0.00)

6.8619 (0.00)

5.7314 (0.01)

Low-Net Working-Capital Firms (N=36) 4.4293 (0.05)

4.3425 (0.32)

5.0631 (0.26)

13.2730 (0.00)

9.5253 (0.00)

*Significantly different from the alternative classification at the 0.10 level

Table 7 Probit model of the choice to tie compensation to the VBM metric. The dependent variable takes the value of 1 if the firm bases compensation on a VBM metric and zero if not, for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. We present p-values for significance in parentheses.

Entire Sample (N=84)

Firms With Data in All Years (N=72)

Intercept 0.6078* (0.09)

0.6375* (0.09)

Residual Income/Invested Capital in the year prior to adoption

1.4858*** (0.00)

1.4098*** (0.00)

VBM adoption accompanies a restructuring (0/1)

-0.0115 (0.92)

-0.0496 (0.60)

Ln (assets) -0.0397 (0.36)

-0.0301 (0.47)

Inside ownership (% of shares) 0.2439 (0.41)

0.3157 (0.26)

Industry-adjusted capital expenditures/sales 1.0180 (0.12)

1.4602** (0.03)

Market-to-book asset ratio -0.1991*** (0.00)

-0.1825*** (0.01)

Net working capital/assets -0.7062* (0.06)

-1.0257*** (0.01)

Another firm in the same 2-digit SIC code bases compensation on VBM in the previous three years (0/1)

0.1564* (0.09)

0.0861 (0.34)

Pseudo R2 0.2171 0.2998

*** Significant at the 0.01 level. ** Significant at the 0.05 level. * Significant at the 0.10 level.

Table 8 Heckman two-stage selection models of the effects of the adoption of value-based management on residual income. The dependent variable is residual income as a percentage of invested capital for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. The probit model in Table 7 is the first stage of the model. To mitigate problems with outliers, we winsorize residual income at the fifth and ninety-fifth percentiles. p-values, based on consistent estimates of standard errors, are in parentheses. R2s are estimated by maximum likelihood and are therefore not bounded by 0 and 1.

All Firms in the Sample Firms with Data for Five Years

Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5

Intercept 0.4711*** (0.00)

0.5069***(0.01)

0.3661***(0.05)

0.6902***(0.01)

0.8428*** (0.01)

0.5349***(0.00)

0.5770***(0.01)

0.4409** (0.03)

0.7481***(0.01)

0.8250 (0.01)

VBM used with restructuring (0/1)

-0.0297 (0.41)

0.0185 (0.68)

0.0821** (0.05)

0.0245 (0.42)

0.0132 (0.85)

-0.0510 (0.22)

0.0021** (0.04)

0.0793* (0.09)

0.0097 (0.15)

0.0053 (0.08)

VBM used for compensation (0/1)

-0.1552* (0.06)

-0.2501***(0.01)

-0.0700 (0.47)

-0.1333 (0.32)

-0.1649 (0.29)

-0.1703** (0.05)

-0.2535** (0.02)

-0.1252 (0.21)

-0.1305 (0.35)

-0.1495 (0.33)

Inside Ownership (% of shares) -0.0100 (0.92)

0.0974 (0.42)

0.2208** (0.05)

0.0887 (0.57)

-0.0100 (0.96)

0.0013 (0.99)

0.1133 (0.37)

0.2691** (0.02)

0.1015 (0.54)

-0.0018 (0.99)

Capital Expenditures/Sales -0.1616 (0.24)

-0.0269 (0.99)

-0.1820 (0.27)

-0.1675 (0.47)

-0.0006 (0.99)

-0.1900 (0.19)

-0.0074 (0.97)

-0.2024 (0.23)

-0.1836 (0.44)

0.0131 (0.96)

Market-to-book asset ratio -0.0076 (0.70)

-0.0205 (0.39

0.0129 (0.56)

-0.0243 (0.43)

-0.0219 (0.55)

-0.0047 (0.82)

-0.0294 (0.24)

0.0055 (0.82)

-00295 (0.38)

-0.0207 (0.57)

Net Working Capital/Assets -0.2754* (0.06)

-0.1796 (0.32)

-0.2379 (0.16)

-0.3232 (0.17)

-0.4962* (0.08)

-0.3416** (0.05)

-0.2072 (0.31)

-0.2891 (0.14)

-0.3774 (0.17)

-0.5203 (0.08)

Log (Assets) -0.0314** (0.05)

-0.0371* (0.06)

-0.0453***(0.01)

-0.0589***(0.02)

-0.0758***(0.01)

-0.0356** (0.04)

-0.0429** (0.04)

-0.0489***(0.01)

-0.0631** (0.02)

-0.0735***(0.01)

Inverse Mills Ratio 0.1027** (0.05)

0.1598***(0.01)

0.0254 (0.68)

0.0632 (0.46)

0.1049 (0.30)

0.1168** (0.03)

0.1570***(0.01)

0.0451 (0.48)

0.0432 (0.63)

0.0937 (0.33)

Adjusted R2 0.0257 0.1079 0.0771 -0.0236 -0.0217 0.0479 0.1343 0.1152 -0.0236 -0.0241

Number of Observations 84 84 84 79 72 72 72 72 72 72

*** Significant at the 0.01 level. ** Significant at the 0.05 level. * Significant at the 0.10 level.

Table 9 Effects of the adoption of value-based management on capital expenditures. We present the median change in capital expenditures as a percentage of sales relative to the industry median for 84 firms (and 72 firms available in all years) that adopt value-based management systems in 1984-1997. We classify a firm as a high-capital-expenditure firm if the capital expenditures divided by sales in the year before adoption exceeds the sample median. High-growth firms have market-to-book-asset ratios in the year before adoption above the sample median. We base p-values (in parentheses) on non-parametric signed rank tests and test for differences between classifications using two-sample Wilcoxon tests. Change from year i to year j t-1 to t+1 t-1 to t+2 t-1 to t+3 t-1 to t+4 t-1 to t+5

Entire Sample

All Firms 0.0692 (0.28) N=84

-0.6459 (0.02) N=84

-0.7355 (0.00) N=84

-1.1896 (0.00) N=79

-1.3238 (0.00) N=72

High-Capital-Expenditure Firms -0.8512 (0.11) N=42

-1.4371 (0.00) N=42

-1.4940 (0.01) N=42

-1.9747 (0.01) N=38

-3.7318 (0.00) N=33

Low-Capital Expenditure Firms 0.1405 (0.81) N=42

0.0142 (0.97) N=42

-0.3127 (0.19) N=42

-0.6088 (0.13) N=41

-0.7919 (0.01) N=39

p-values for difference 0.12 0.01 0.06 0.01 0.00

High-growth Firms -0.3991 (0.13) N=42

-0.8828 (0.05) N=42

-1.3062 (0.03) N=42

-0.9092 (0.05) N=39

-1.5431 (0.02) N=34

Low-growth Firms 0.1503 (0.97) N=42

-0.2258 (0.20) N=42

-0.4510 (0.06) N=42

-1.2489 (0.01) N=40

-1.1719 (0.00) N=38

p-values for difference 0.32 0.43 0.36 0.96 0.84

Sample of 72 Firms Available in All Years

All Firms (N=72) -0.0112 (0.36)

-0.8285 (0.01)

-0.7616 (0.00)

-1.2489 (0.00)

-1.3238 (0.00)

High-Capital-Expenditure Firms (N=36) -0.4190 (0.34)

-1.4821 (0.00)

-1.4651 (0.00)

-2.4377 (0.00)

-3.2858 (0.00)

Low-Capital Expenditure Firms (n=36) 0.1023 (0.72)

-0.0356 (0.70)

0.3127 (0.20)

-0.6198 (0.15)

-0.7262 (0.04)

p-values for difference 0.46 0.01 0.03 0.00 0.00

High-growth Firms (N=36) -0.3991 (0.22)

-1.2001 (0.02)

-1.3062 (0.02

-1.0773 (0.01)

-1.5431 (0.01)

Low-growth Firms (n=36) 0.1336 (0.98)

-0.2258 (0.16)

-0.4510 (0.06)

-1.3034 (0.00)

-1.1719 (0.00)

p-values for difference 0.45 0.25 0.21 0.74 0.98