ceo incentives, cash flow, and investment

CEO Incentives, Cash Flow, and InvestmentAuthor(s): John Paul Broussard, Sheree A. Buchenroth and Eugene A. PilotteSource: Financial Management, Vol. 33, No. 2 (Summer, 2004), pp. 51-70Published by: Wiley on behalf of the Financial Management Association InternationalStable URL: http://www.jstor.org/stable/3666158 .Accessed: 16/06/2014 06:25

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CEO Incentives, Cash Flow, and Investment

John Paul Broussard, Sheree A. Buchenroth, and Eugene A. Pilotte*

We estimate the impact ofchiefexecutive officer (CEO) incentives on the sensitivity ofinvestment to cash flow during a period of strong economic growth. Our measure of the alignment of managers' and shareholders' interests, pay-performance sensitivity (PPS), incorporates both stock and stock option holdings. Contrary to prior studies, we find that the dominant effect of increasing alignment is to reduce the overinvestment offree cash flow. We find no evidence that incentives exacerbate the severity offinancial constraints. We find some evidence that PPS

helps reduce the underinvestment of cash flow due to managerial shirking.

In this article, we estimate the impact of increasing pay-performance sensitivity (PPS) on the sensitivity of investment to cash flow. Our motivation is to provide additional evidence on the usefulness of executive compensation in reducing agency costs and on the influence of managerial incentives on the severity of financial constraints on investment.

Our sample period is based on compensation data for the fiscal year-ends 1993-1997, a time period characterized by strong economic growth. Because strong economic environments contribute to the ability of firms to produce cash flow, firms in our sample are more likely to show evidence of free cash flow problems and are less likely to be financially constrained.

Using detailed compensation data over our sample period, we estimate chief executive officer (CEO) incentives that incorporate both stock and stock option holdings to measure the alignment of managers' and shareholders' interests. Rather than focusing on the relation between PPS and the level of investment, we concentrate on the impact of PPS on the subsequent investment of cash flow. We contend that estimating the impact of available cash flow on investment provides a more direct test of the influence of compensation on the tendency to overinvest free cash flow.

In addition to estimating the impact of PPS for the average firm, we subdivide our sample. Doing so enables us to isolate the hypothesized effects of agency costs from those of financial constraints.

For our full sample, we find the sensitivity of investment to cash flow is reduced as PPS increases. This finding is consistent with the hypothesis that a stronger alignment of managers' and shareholders' interests reduces the tendency of managers to invest available free cash flow. Our examination of subsamples based on Tobin's Q and commercial paper ratings confirms this conclusion.

Consistent with free cash flow theory, we find that the negative impact of PPS on investment- cash flow sensitivities is concentrated in low Q firms. The negative relation is concentrated in unrated firms, which are the firms most likely to suffer from the information asymmetries predicted to produce a positive relation. We find no evidence that incentives exacerbate the severity of financial constraints, even for a subsample of firms with ample investment opportunities and a high level of information asymmetry (high Q firms with no commercial paper rating). We do find

We thank Charles Hadlock, William T Moore, an anonymous referee, and session participants at the 2003 Eastern Finance Association Meeting for many helpful comments. *John Paul Broussard is an Associate Professor of Finance at Rutgers University, Sheree A. Buchenroth was an Assistant Professor of Finance at West Chester University, and Eugene A. Pilotte is a Professor of Finance at Rutgers University.

Financial Management * Summer 2004 * pages 51 - 70


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52 Financial Management * Summer 2004

subsample evidence that PPS plays a role in reducing the underinvestment of cash flow due to managerial shirking.

The article is organized as follows. Section I summarizes the background literature and testable implications. Section II describes the data panel. Sections III and IV present empirical results for the full sample and subsamples, respectively. Section V is a discussion of alternative

empirical specifications and endogeneity concerns. Section VI concludes.

I. Background Literature and Testable Implications

Evidence that managers' incentives affect the investment decision is provided in Hadlock

(1998) and Aggarwal and Samwick (1999). Both studies reject the hypothesis that equity based incentives alleviate the agency costs of free cash flow.

Hadlock's conclusions are based on estimates of the impact of insider shareholdings on the sensitivity of investment to cash flow during the sample period 1973-1976. For insider

holdings below 5% of shares outstanding, he finds a positive relation between insider

holdings and investment-cash flow sensitivities. The relation is negative for higher ownership levels. Hadlock attributes the positive relation at low ownership levels to the hypothesis that linking managers' interests more closely to those of stockholders increases the severity of the financial constraints caused by asymmetric information. He suggests that the reversal of the relation at high ownership levels occurs because very high ownership stakes entrench

managers, weakening their incentives to act in shareholders' interests.

Aggarwal and Samwick base their analysis on PPS, which incorporates top managers' stock and option compensation. Using compensation data for the period 1993-1997, they find that both Tobin's Q and investment are increasing in PPS, and conclude that increases in PPS alleviate the agency costs of underinvestment due to managerial shirking.

Many researchers find that capital spending by manufacturing firms is positively related to current period cash flow. For examples see Fazzari and Athey (1987), Fazzari, Hubbard, and Petersen (1988), Devereaux and Schiantarelli (1990), Oliner and Rudebusch (1992), Vogt (1994), Kaplan and Zingales (1997), Hsiao and Tahmiscioglu (1997), and Hadlock (1998). Potential

explanations for the observed sensitivity of investment to cash flow are agency costs and

asymmetric information.

A. Agency Costs and Overinvestment (Empire Building)

Jensen and Meckling (1976) develop the idea that managers' pursuit of their own self- interest causes them to choose a level of investment different from the optimum level for a

completely manager-owned firm. Jensen (1986) relates the agency problem directly to the

ability of the firm to produce free cash flow (cash flow in excess of that needed to fund

positive NPV projects). The tendency to overinvest free cash flow is one potential explanation for the positive relation between cash flow and investment.

Studies by Jensen and Meckling (1976), Murphy (1985), and Jensen and Murphy (1990) suggest that agency costs can be reduced through incentive compensation schemes that insure managers benefit directly from increases in shareholder wealth. Jensen and Murphy (1990) and Murphy (1993) develop a measure of the degree of alignment provided by executive compensation contracts. These studies define pay-performance-sensitivity as the dollar change in chief executive officer (CEO) wealth per $1,000 change in shareholder wealth. The rationale behind PPS as a measure of alignment is that managers who realize a greater



Broussard, Buchenroth, & Pilotte * Ceo Incentives, Cash Flow, and Investment 53

proportion of shareholder wealth changes are more likely to make decisions in shareholders' interests. If increasing PPS does reduce the severity of the agency costs of free cash flow, then the positive relation between cash flow and investment should be strong for firms with low PPS and weak for firms with high PPS. Thus, the free cash flow theory predicts that the sensitivity of cash flow to investment decreases as PPS increases.

B. Agency Costs and Underinvestment (Shirking)

Jensen and Meckling (1976) argue that the most important agency conflict could arise from the fact that as a manager's ownership stake falls, his incentive to search out new profitable investment opportunities decreases. Firms for which the predominant agency problem is one of managerial shirking may tend to invest less available cash flow than will the average firm.

For such firms, increasing PPS to reduce the agency conflict should reduce the tendency to underinvest cash flow. Thus, the relation between cash flow and investment should be more positive when PPS is high than it is when PPS is low, and the sensitivity of cash flow to investment is predicted to increase with PPS.

C. Asymmetric Information, Financial Constraints, and Underinvestment

Myers and Majluf (1984) find that asymmetric information causes external funding to be more costly than it would be in a world of perfect markets. This effect occurs because outsiders cannot distinguish between firms having high versus low quality projects, and so will price every security issue as if it funds an average quality project. Thus, firms pass up some positive NPV projects rather than issue securities for less than they are worth. Myers and Majluf conclude that this problem is avoided when the firm can fund projects out of available cash. Thus, the "lemons premium" associated with external finance can cause investment to be sensitive to the availability of internal funding for the project.

A key assumption in the Myers and Majluf analysis is that management acts in the interests of existing shareholders. Dybvig and Zender (1991) point out that this assumption is critical for information asymmetry to produce underinvestment. Hadlock (1998) provides a model in which increasing managements' share of shareholder wealth changes exacerbates the Myers/Majluf underinvestment problem. His analysis implies that the sensitivity of investment to cash flow is strongest when the interests of managers and shareholders are perfectly aligned. Thus, Hadlock predicts that the sensitivity of investment to cash flow increases as PPS increases.

Kaplan and Zingales (1997) challenge the idea that high investment-cash flow sensitivities are evidence of financial constraints. For the 49 low-dividend firms classified by Fazzari, Hubbard, and Petersen (1988) as financially constrained, Kaplan and Zingales find evidence that the relation between sensitivities and liquidity measures is nonmonotonic. Moreover, they find that the most liquid firms have the highest estimated investment-cash flow sensitivities.

Povel and Raith (2001) explain the findings of Kaplan and Zingales with a theoretical model that predicts a U-shaped relation between investment and cash flow. Povel and Raith attribute the U-shape to firms' responses to the combined influences of asymmetric information and the financial distress caused by large negative cash flows. They argue that an accurate estimate of the impact of asymmetric information on investment-cash flow sensitivities requires either a careful choice of sample-splitting criteria, or eliminating firms that have large negative cash flows from the sample, or both. We incorporate their suggestions into our empirical analysis.




D. Isolating the Influence of Alternative Theories

We use our full sample to estimate the relation between PPS and the sensitivity of investment to cash flow. However, this approach only estimates the impact due to the combined influence of empire building, shirking, and asymmetric information. To isolate the influence of each theory, we subdivide the sample, using measures of investment opportunities and asymmetric information.

Our first sample division is based on Tobin's Q. High Q firms are likely to have valuable investment opportunities, so underinvestment problems should be concentrated in high Q firms. Low Q firms are less likely to have valuable investment opportunities, so overinvestment problems should be concentrated in low Q firms.

We base our second sample division on whether or not sample firms have rated commercial paper. Our use of commercial paper ratings is motivated by the fact that the disclosure requirements associated with obtaining a commercial paper rating require rated firms to reveal a large amount of information. Thus, rated firms are less likely than unrated firms to have problems associated with information asymmetry.'

We also estimate results for a four-way split based on both Q and commercial paper ratings. The advantage of a four-way split is that it should isolate the influence of underinvestment that is due to asymmetric information from underinvestment due to managerial shirking. High Q firms that have rated commercial paper are unlikely to have problems of asymmetric information. Underinvestment by such firms is most likely the result of managerial shirking. On the other hand, high Q firms that are not rated are more likely to underinvest because of financial constraints due to asymmetric information.

II. Construction of Panel, Test Design, and Descriptive Statistics

Here we discuss the construction of the data panel, empirical model, and descriptive statistics.

A. Construction of Panel and Test Design

We use Standard and Poor's ExecuComp database to construct our measure of pay- performance sensitivity. ExecuComp contains data on all aspects of compensation for top executives at each of the firms in the S&P 500, S&P Midcap 400, and S&P Smallcap 600. Other data are drawn from the Compustat database.

We use dates provided by ExecuComp to identify chief executive officers (CEOs) of firms at the end of each fiscal year-end from 1993 to 1997. We use this approach rather than the CEO flag provided by ExecuComp, because the CEO flag identifies the individual who was CEO for the majority of the fiscal year. We want to identify the CEO at the end of each year so that we can examine the impact of beginning-of-year CEO incentives on behavior in the

following year. We find 1,794 firms that report compensation data for at least one of these fiscal year-ends

and can also be matched to Compustat data. We further limit the sample to manufacturing firms

(two-digit SIC codes between 20 and 39) that have a balanced panel of data. These sample selection criteria result in a final sample of 382 firms with five years of data for each firm.

Prior studies, such as Murphy (1998) and Aggarwal and Samwick (1999), find that the

'Our choice of commercial paper ratings over alternative measures of information asymmetry, is supported by the

analysis of Povel and Raith (2001), who show that commonly used alternative measures are likely to produce incorrect inferences. They caution against measures that are strongly correlated with net worth or internal funds. Kaplan and

Zingales (1997) also argue that most alternative sorting criteria provide predictions that are theoretically ambiguous.




majority of the sensitivity of executive compensation to changes in shareholder wealth is due to executives' holdings of common stock and stock options. Therefore, we construct our PPS measure using data on CEO holdings of common stock and stock options. Stock PPS is the percent of total shares outstanding that are held by the CEO multiplied by 1,000. Multiplying by 1,000 produces a measure that indicates the change in CEO wealth per $1,000 change in shareholder wealth. The option PPS is the number of options held by the CEO multiplied by the options delta from the Black-Scholes (1973) model multiplied by 1,000. Total PPS is the sum of Stock PPS and Options PPS. Appendix A provides the details of our calculations of Stock PPS and Options PPS.

We base our empirical analysis on an augmented version of the standard model of capital investment. Because we are interested in the impact of CEO incentives on investment, we match ExecuComp data for fiscal years ending from 1993-1997 with Compustat data for fiscal years ending 1994-1998. Thus, we estimate the impact of beginning-of-period CEO incentives on subsequent investment. Our empirical model is:

I S CASH CF CF

=a + a2Q,1 + CA3 C4+

14 + c5PPS,, K (K)0,K K )0 ( K 0,

+ a6 ln(MVE,, ) jF) + a,7TENURE,,, -

+ C

, + i,

+ E(1) K K

tK

0,

For firm i and year t, I is investment (capital expenditures), K is the beginning of period capital stock (net property, plant and equipment), S is sales, Q is the beginning of period estimate of Tobin's Q, CASH is beginning of period cash plus marketable securities, CF is cash flow net of common and preferred dividends, PPS is beginning of period Total PPS for the firm's CEO, MVE is the beginning of period market value of equity, and TENURE is the CEO's length of tenure (years) in that office. The parameter

1t is a year fixed effect, Xi is a firm

fixed effect, and si,t is the error term. The firm and year fixed effects capture differences in capital spending across years and firms that are not captured by our independent variables. For variables other than PPS and TENURE (based on ExecuComp data), Data Appendix B provides details of variable definitions and a list of the Compustat data items we use to calculate each variable.

Including sales in the empirical model is motivated by the flexible accelerator model in which sales is the proxy for changes in product demand. We include Tobin's Q as a proxy for the profitability of investment. We include cash and marketable securities to capture the impact of available financial slack on investment and because firms often stockpile cash for planned investment.

The coefficient a4 on the cash flow variable and the coefficients a5, a6, and a7 on the three interaction variables combine to estimate the sensitivity of capital spending to cash flow. The coefficient a5 captures the influence of PPS on the sensitivity of investment to cash flow and is the parameter of interest for our study. If increasing PPS decreases the sensitivity of investment to cash flow, a5 will be negative. If increasing PPS increases the sensitivity of investment to cash flow, ac will be positive.

The coefficients a6 and a7, respectively, capture the influence of firm size and CEO tenure on the sensitivity of investment to cash flow. We control for firm size because the managers of small firms tend to have high PPS, and firm size can be a proxy for asymmetric information and/or agency costs. We control for CEO tenure because both PPS and agency costs are




related to the CEO's proximity to retirement.2 The controls for size and tenure insure that the coefficient

x5 on the PPS-cash flow interaction variable captures the influence of PPS rather

than the influences of size and tenure. As a robustness check, we also re-estimate Equation (1) after replacing PPSi,t

with CDFIt the cumulative distribution function of PPSi,t for each year. Thus, CDFi,t ranges from zero for the firm with the lowest PPS in year t to one for the firm with the highest PPS in year t. Results based on CDFi,t should be less sensitive to the influence of outlying values of PPSi,.

As a further robustness check, we estimate regressions that include and regressions that exclude firm-year observations where cash flow is negative. These regressions address concerns raised by Povel and Raith (2001), who argue that the relation between investment and cash flow is U-shaped and only expected to be positive when firms have positive cash flows.

B. Summary Statistics

Table I contains statistics that describe the results of estimating Stock PPS, Options PPS, and Total PPS for fiscal year-ends 1993-1997, for manufacturing firms that had a complete set of desired ExecuComp and Compustat data. The mean Total PPS for the full sample is 32.73, with a Stock PPS of 24.51 and an Options PPS of 8.21. The statistics indicate substantial dispersion in Total PPS across sample firms.

Table I also shows summary statistics for dependent and independent variables other than PPS. The means indicate that capital expenditures averaged 29% of beginning capital and cash flow averaged 35% of capital. The median values of capital expenditures and cash flow are similar to the means. Both the means and medians suggest that over the five-year sample period, our average sample firm generated enough cash flow to fund all of its capital spending.

The mean (median) holdings of cash and marketable securities are 12% (5%) of capital. That this financial slack may play a role in smoothing investment spending is indicated by an examination of the standard deviations of capital spending and cash flow. The volatility of capital spending is about one-third that of cash flow.

Summary statistics for Q, MVE, and TENURE indicate substantial variation in these variables, confirming their potential importance in providing adequate controls.

Ill. Empirical Results for Full Sample

Table II reports the empirical results for the full sample of 382 manufacturing firms having a complete panel of data. The null hypothesis for t-statistics (in parentheses) is that the coefficient does not differ significantly from zero. The first column of Table II reports the results of estimating Equation (1) using the complete data panel. The second column reports the results of estimating Regression (1) after excluding firm-year observations where cash flow is negative. As a check on the sensitivity of results to extreme values of PPS, column three reports results of estimating Equation (1) after replacing PPS.,i with CDFt the cumulative distribution function of PPS.,, for each year. The fourth column reports results of estimating Regression (1) based on CDF, t after excluding firm-year observations where cash flow is

negative. Due to space considerations, we do not report our estimates of firm or year fixed

2As CEOs approach retirement and their decision horizon shortens, they may be less inclined to exertions that may reward their successors. They are also more likely to be entrenched and less likely to face the discipline of the labor market in the future. For evidence that the threat of firing decreases as managers approach retirement see Jensen and Murphy (1990). For recent evidence regarding the relations of PPS components to CEO decision- horizons and firm size see Bryan, Hwang, and Lilien (2000).




Table I. Summary Statistics for Dependent and Independent Variables

The data are for fiscal years between 1994 and 1998. Some data are fiscal year-end, other data are year- beginning (end of prior fiscal year). Results are for 382 manufacturing firms that have all ExecuComp and Compustat data required for our panel data analysis. PPS is the dollar change in CEO wealth per $1,000 change in stockholder wealth. Total PPS is the sum of Stock PPS and Options PPS. We define Stock PPS as the percentage of shares outstanding held by the CEO multiplied by 1,000. Options PPS is the option delta from the Black-Scholes (1973) model multiplied by the number of options held by the CEO multiplied by 1,000. PPSs are estimated at the fiscal year-beginning. I is capital expenditures for the fiscal year. K is the beginning-of-period capital stock, as measured by net property, plant and equipment. S is the level of sales for the fiscal year. Q is the beginning-of-year estimate of Tobin's Q. CASH is beginning-of-year cash and marketable securities. CF is the cash flow for the year, defined as net income after tax plus depreciation less common and preferred dividends. MVE is the beginning-of-year market value of equity ($millions). TENURE is the CEO's tenure in office (years).

Std. Percentiles Variable Mean Dev. 10t h 25th 50th 75th 90th TOTAL PPS 32.73 63.94 1.21 3.46 10.17 28.36 78.86 STOCK PPS 24.51 60.77 0.15 0.60 2.54 14.18 69.00 OPTIONS PPS 8.21 16.97 0.18 1.12 3.69 9.52 20.13 I/K 0.29 0.28 0.10 0.15 0.22 0.34 0.55 S/K 5.61 6.55 1.53 2.54 4.03 6.45 10.83 Q 1.52 1.27 0.54 0.78 1.14 1.80 2.94 CASH/K 0.12 0.17 0.01 0.02 0.05 0.17 0.34 CF/K 0.35 0.96 0.02 0.16 0.31 0.53 0.97 MVE 4,488 12,332 192 383 1,087 3,305 8,930 TENURE 8.00 7.36 1.06 2.75 6.00 10.84 17.49

effects. Standard errors are adjusted for heteroskedasticity. Our major finding is that in each of the four versions of Equation (1) that we estimate, the

estimated coefficient on the PPS-cash flow interaction variable is negative and significant at the 0.05 level. Thus, for the full sample, the sensitivity of investment to cash flow reduces as PPS increases. Our results indicate that the dominant influence of PPS on the sensitivity of investment to cash flow is through a reduction in the tendency of sample firms to overinvest free cash flow. This result is not sensitive to the definition of the PPS-cash flow interaction variable, nor is it sensitive to the exclusion of firm-year observations that have negative cash flows.

The estimated coefficient on the cash flow variable combines with the coefficients on the three interaction variables to estimate the net sensitivity of capital spending to cash flow. The coefficient on CF/K is always positive. It is significant at either the 0.01 or 0.05 level in three of the four regressions. The coefficient on the cash flow-size interaction variable is always negative. It is significant at the 0.05 level in two regressions and at the 0.10 level in one regression. The coefficient on the tenure-cash flow interaction variable is always positive and significant at the 0.01 level. Thus, investment appears to be sensitive to (increase with) cash flow, while the sensitivity of investment to cash flow increases with CEO tenure and decreases with both firm size and PPS.

We interpret the positive coefficient on the tenure-cash flow interaction variable as further evidence that agency costs play a role in the determination of investment-cash flow sensitivities, because CEOs with long tenures are more likely to be entrenched in their positions and less likely to be concerned with facing the discipline of the labor market in the future.




Table II. Results of Panel Regression Estimation for the Full Sample

This table reports the estimated coefficients from regressing investment to capital, I/K, on various explanatory variables. CDF is the cumulative distribution function of PPS for firm i in year t. Thus, CDF is zero if firm i has the lowest PPS value at the beginning of year t (end of prior fiscal year) and one if firm i has the highest PPS value at the beginning of year t. All variables are defined in Table I. The full sample consists of 382 firms that have data for each of five years from 1994 to 1998, providing a total of 1,910 firm-year observations. The sample labeled CF > zero excludes firm-year observations where cash flow is less than zero. Q, CASH, K, MVE, TENURE, PPS, and CDF are for the beginning of period (prior year-end). All other variables are for year-end. We estimate, but do not report, year and firm fixed effects. We base t-statistics (in parentheses) on heteroskedasticity-consistent standard errors.

Explanatory Observation Observations Variable Full Sample with CF > zero Full Sample with CF > zero S/K .0338 .0214 .0323 .0210

(6.68)*** (2.81)*** (6.15)*** (2.75)*** Q .0649 .0617 .0681 .0677

(4.32)*** (4.61)*** (4.34)*** (4.54)*** CASH/K .6019 .5049 .6007 .4845

(4.65)*** (3.97)*** (4.57)*** (3.90)*** CF/K .1104 .4564 .2096 .5637

(1.33) (2.80)*** (2.39)** (3.96)*** PPS*(CF/K) -.0006 -.0009

(-2.08)** (-2.45)** CDF*(CF/K) -.1629 -.2445

(-2.30)** (-2.81)*** ln(MVE)*(CF/K) -.0225 -.0554 -.0279 -.0595

(-1.45) (-2.40)** (-1.86)* (-2.80)*** TENURE*(CF/K) .0125 .0172 .0139 .0199

(2.68)*** (2.69)*** (3.04)*** (3.30)*** Adjusted R2 .56 .65 .56 .65 N 1910 1725 1910 1725

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

The negative coefficient on the size-cash flow interaction variable is consistent with two interpretations. First, because small firms are generally associated with greater information asymmetries, the negative coefficient is consistent with the hypothesis that greater asymmetric information increases the investment-cash flow sensitivities of small firms. Second, because large firms are generally associated with greater agency problems, the negative coefficient is also consistent with the hypothesis that greater shirking by managers of large firms decreases those firms' tendencies to invest available cash flow.

Our control variables indicate that capital spending is significant and positively related to sales, Q, and holdings of cash and marketable securities. Thus, investment appears to increase with product demand, the profitability of investment, and available financial slack.

IV. Empirical Results for Subsamples

Our full sample estimates of the impact of PPS on the sensitivity of investment to cash




flow illustrate the combined influence of empire building, shirking, and asymmetric information. These estimates indicate that the role of PPS in reducing the agency costs of free cash flow is the dominant influence, but do not rule out influences due to shirking or asymmetric information. To better isolate the potential influences of alternative theories, we subdivide the sample by using Q as a measure of investment opportunities and commercial paper ratings as an indicator of the degree of asymmetric information.

We report results only for the version of Equation (1) that utilizes the PPS interaction term and the full data panel. We estimate the results for the alternative specifications that use the CDF interaction variable and/or exclude firm-year observations with negative cash flows, but, to save space, do not report them. The alternative specifications support the same conclusions as the reported regressions.

A. Results for Two-Way Sample Divisions

In Table III we report results of two-way sample divisions. The regression coefficients and the t-statistics shown in parentheses are results of estimating regression Equation (1) separately for each subsample (four regressions). The null hypothesis for each t-statistic

reported in parentheses is that the coefficient for a subsample does not differ significantly from zero.

We also report the results of joint estimation that tests for significant differences in the coefficients of HIGH Q compared to LOW Q firms and RATED compared to UNRATED firms. This joint estimation uses the full sample and the standard dummy variable technique for estimating differences in slope coefficients between subsamples. Our first dummy variable regression is:

- aI + a&2Q1C, + A +aCF CF\ KK, Ki, K ), j ,K ,0

+a6 ln(MVE,,)C + a,7TENURE,, C + a8D C K Q,(K)1,t K Q

+a9D + oD CASH

K ) + OD -- CF

+ a12D'

PPS, CF

(2) K )Q (K) K )0 K

+a3D ln(MVE,,) + a4D- TENURE,, +, +, +,,

otK(3K ln() -- K

),

where D is a dummy variable that equals one for HIGH Q firms and zero for LOW Q firms. In this empirical specification, the coefficients a, a22,..., aC7, are estimates of the relations between the dependent and independent variables for LOW Q firms. The corresponding coefficients a8, a9, ...,

a4, are estimates of the differences in the relations for HIGH Q compared to LOW Q firms.

The t-statistics for as, 89,..., 14, test the equality of the relations between the dependent and

independent variables for HIGH Q compared to LOW Q firms. In Table III, we show each such t-statistic in brackets in the HIGH Q column below the

coefficient whose difference is tested. In the second dummy variable regression, we repeat this procedure, estimating regression (2) where D is a dummy variable that equals one for RATED firms and zero for UNRATED firms. For this regression, the t-statistics for a8,

a9. a, l4, test the equality of coefficients for RATED compared to UNRATED firms. In Table III,




we show each such t-statistic in brackets in the RATED column below the coefficient whose difference is tested.

Our first sample split is based on Q. We use the mean value of each firm's Q over the five-

year sample period to classify the firm as HIGH Q or LOW Q. We classify a firm as HIGH Q if its five-year mean Q is greater than the median five-year mean Q for all sample firms. Otherwise, we classify the firm as LOW Q. Firms that have valuable investment opportunities should be concentrated in the HIGH Q subsample, so evidence consistent with underinvestment

problems should appear in that subsample. Firms with poor investment opportunities should be concentrated in the LOW Q subsample, so evidence consistent with the overinvestment

problem should be concentrated in LOW Q firms. Table III results for the sample split based on Q indicate that the significant, negative

coefficient on the PPS-cash flow interaction variable is isolated in the LOW Q subsample. For the HIGH Q subsample, the coefficient is positive, as suggested by theories of underinvestment, but is not statistically significant. The difference in the coefficients for LOW Q and HIGH Q firms is significant at the 0.05 level. Thus, the results of the division based on Q confirm our notion that the full sample results are driven by the role of PPS in

reducing the empire building predicted by the agency problem of free cash flow. Our second sample division is based on whether or not sample firms have rated commercial

paper. We designate as RATED (UNRATED) firms that have (do not have) commercial paper rated by Standard and Poor's Corporation during the sample period. We expect that firms classified UNRATED are more likely than firms classified as RATED to have problems associated with information asymmetry.

The Table III results for the sample split based on commercial paper ratings indicate that the significant, negative coefficient on the PPS-cash flow interaction variable is isolated

among firms classified as UNRATED rather than RATED. The difference in the coefficients for RATED and UNRATED firms is significant at the 0.05 level. These findings are contrary to the prediction of a positive impact of PPS on investment-cash flow sensitivities of firms with substantial information asymmetry.

B. Results for Four-Way Sample Division

In Table IV we report results for a four-way sample division based on both Q and commercial

paper ratings. The regression coefficients and the t-statistics reported in parentheses are results of estimating Equation (1) separately for each subsample (four regressions). The null

hypothesis for each t-statistic reported in parentheses is that the coefficient for a subsample does not differ significantly from zero.

The null hypothesis for each t-statistic reported in brackets is that the coefficient for a

subsample does not differ significantly from the corresponding coefficient for LOW Q, UNRATED firms. The bracketed t-statistics are the results of estimating a single dummy variable regression for the full sample. Our approach is as in Regression (2), except that we

expand the regression so that each independent variable interacts separately with each of three (rather than only one) dummy variables.

We define The dummy variables as follows: Dl equals one for LOW Q, RATED firms, zero otherwise; D2 equals one for HIGH Q, UNRATED firms, zero otherwise; and D3 equals one for HIGH Q, RATED firms, zero otherwise.

The advantage of a four-way split is that it should isolate the influence ofunderinvestment that is due to asymmetric information from underinvestment due to managerial shirking. The HIGH Q, RATED subsample should consist of firms with valuable investment opportunities,




Table III. Results of Panel Regressions for Two-way Sample Divisions

This table reports the estimated coefficients from regressing investment to capital, I/K, on various explanatory variables. We base our sample divisions on Q and on commercial paper ratings. We base the division by Q on each firm's five-year average value of Q. If that five-year average Q is below the median for all sample firms, then we classify the firm as LOW Q. Otherwise we classify it as HIGH Q. We classify a firm as RATED if it has rated commercial paper during the five-year sample period, otherwise, as UNRATED. Other variables are defined in Table I. Q, CASH, K, MVE, TENURE, and PPS are for the beginning of period (prior year-end). All other variables are for year-end. We estimate but do not report year and firm fixed effects. We base student's t-statistics on heteroskedasticity-consistent standard errors. We derive the coefficients and the t-statistics reported in parentheses by estimating separate regressions for each of the four subsamples. The null hypothesis for t-statistics in parentheses is that the coefficient for a subsample does not differ significantly from zero. We also report results of joint estimation that tests for significant differences in coefficients for LOW compared to HIGH Q firms and RATED compared to UNRATED firms. The joint specification uses the full sample and the standard dummy variable technique for estimating differences in slope coefficients between subsamples. Selected t-statistics from the two dummy variable regressions appear in brackets. The null hypothesis for the bracketed t-statistics below the coefficients for HIGH Q firms is that the coefficient for HIGH Q firms does not differ significantly from the corresponding coefficient for LOW Q firms. The null hypothesis for the bracketed t-statistics below the coefficients for RATED firms is that the coefficient for RATED firms does not differ significantly from the corresponding coefficient for UNRATED firms.

Explanatory LOW Q HIGH Q UNRATED RATED Variable Subsample Subsample Subsample Subsample S/K .0331 .0563 .0331 .0392

(4.74)*** (8.03)*** (6.22)*** (6.82)*** [2.35]* [0.87]

Q .1109 .0489 .0741 .0136 (3.91)*** (3.04)*** (4.26)*** (2.05)**

[-2.26]** [-2.71]*** CASH/K .3735 .5054 .7554 -.0662

(2.68)*** (2.84)*** (4.93)*** (-0.96) [0.56] [-4.88]***

CF/K .1626 .0734 .1629 -.0645 (0.92) (0.92) (1.65)* (-0.67)

[-0.47] [-1.88]* PPSo(CF/K) -.0010 .0005 -.0005 .0005

(-2.50)** (1.07) (-1.99)** (1.23) [2.44]** [2.21]**

ln(MVE)*(CF/K) -.0282 -.0227 -.0328 .0081 (-1.14) (-1.40) (-1.73)* (0.72)

[0.20] [2.01]** TENURE*(CF/K) .0125 .0089 .0135 .0012

(3.67)*** (1.98)** (2.64)*** (0.50) [-0.62] [-2.28]**

Adjusted R2 .60 .58 .55 .65

N 955 955 1265 645





Table IV. Results of Panel Regressions for Four-Way Sample Division

This table reports the estimated coefficients from regressing investment to capital, I/K, on various explanatory variables. We base the sample division on Q and on commercial paper ratings. We base the division by Q on each firm's five-year average value of Q. If that five-year average Q is below the median for all sample firms, then we classify the firm as LOW Q. Otherwise, we classify it as HIGH Q. We classify a firm as RATED if it has rated commercial paper during the five-year sample period, otherwise as UNRATED. Other variables are defined in Table I. Q, CASH, K, MVE, TENURE, and PPS are for the beginning of period (prior year-end). All other variables are for year-end. We estimate but do not report year and firm fixed effects. We base t-statistics on heteroskedasticity-consistent standard errors. We derive the coefficients and the t-statistics reported in parentheses by estimating separate regressions for each of the four subsamples. The null hypothesis for t-statistics in parentheses is that the coefficient for a subsample does not differ significantly from zero. We also report results of joint estimation that tests for significant differences in coefficients across the subsamples. The joint specification uses the full sample and the standard dummy variable technique for estimating differences in slope coefficients between subsamples. Selected t-statistics from the dummy variable regression appear in brackets. The null hypothesis for each bracketed t-statistic is that the coefficient does not differ significantly from the corresponding coefficient for LOW Q, UNRATED firms.

Explanatory LOW Q, LOW Q, HIGH Q, HIGH Q, Variable UNRATED RATED UNRATED RATED S/K .0331 .0574 .0593 .0388

(4.51)*** (4.39)*** (7.44)*** (6.40)*** [1.49] [2.41]** [0.61]

Q .1108 .1047 .0550 .0165 (3.5 1)*** (2.51)** (2.96)*** (2.18)**

[0.54] [-1.65]* [-3.09]** CASH/K .4422 -.0512 .6775 -.0361

(2.69)* (-.046) (3.18) (-0.41) [-2.33]** [0.79] [-2.61]***

CF/K .3033 -.1254 .1095 -.0525 (1.41) (-0.46) (1.17) (-0.41)

[-1.16] [-0.96] [-1.44] PPS*(CF/K) -.0010 -.0012 .0005 .0013

(-2.37)** (-2.25)** (1.05) (2.15)** [-0.07] [2.37]** [3.02]***

ln(MVE)*(CF/K) -.0576 .2105 -.0305 .0041 (-1.72)* (0.61) (-1.60) (0.30)

[1.56] [0.85] [1.74]* TENURE*(CF/K) .0153 -.0038 .0096 .0033

(3.87)*** (-0.58) (1.99)** (1.33) [-2.55]** [-0.96] [-2.66]***

Adjusted R2 .60 .59 .54 .70 N 670 285 595 360

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

*Significant at the 0.10 level.

ready access to capital, and little information asymmetry. These firms are unlikely to be

financially constrained, but may be prone to managerial shirking. On the other hand, the HIGH Q, UNRATED subsample should comprise firms that have valuable investment

opportunities, a high degree of information asymmetry, and limited access to capital. We

predict that increasing PPS for firms in this subsample will increase the severity of financial




constraints attributable to asymmetric information. Whether or not the commercial paper rating process will have any impact on the

overinvestment problem that is concentrated in LOW Q firms is difficult to predict, apriori. The need for RATED firms to have the high levels of liquidity that are required to maintain commercial paper ratings could discourage overinvestment. In that case, the negative impact of PPS on investment-cash flow sensitivity should be concentrated in the LOW Q, UNRATED

subsample. On the other hand, high liquidity and the ability to obtain a commercial paper rating could both be indicators of the ability to generate free cash flow, in which case evidence of overinvestment should also be evident in the LOW Q, RATED subsample.

The results of the four-way sample split produce our only significant and positive coefficient on the PPS-cash flow interaction variable. The significant, positive coefficient is for the HIGH Q, RATED subsample. This finding is consistent with the idea that increasing PPS increases the reinvestment of cash flow by the managers most prone to shirk. The PPS-cash flow interaction variable is positive but not significant for the HIGH Q, UNRATED subsample. Thus, we find no evidence that PPS increases the severity of financial constraints for a subsample of firms with ample investment opportunities and a high level of information asymmetry.

Our results for both the LOW Q, RATED and LOW Q, UNRATED subsamples indicate a significant negative impact of PPS on investment-cash flow sensitivity. Thus, the commercial

paper rating process appears to have little relation to the severity of the agency costs of free cash flow.

Our tests of differences in the coefficients on the PPS-cash flow interaction variable show no significant differences for the LOW Q, UNRATED and LOW Q, RATED subsamples. However, coefficients for the HIGH Q, UNRATED and HIGH Q, RATED subsamples do differ significantly from the coefficient for the LOW Q, UNRATED subsample.

V. Alternative Specifications and Endogeneity Concerns

Here, we discuss three issues related to the robustness of our results.

A. Alternative Empirical Specification with Additional Control Variables

An implicit assumption of Equation (1) is that PPS, In(MVE), and TENURE affect investment only through their interaction with cash flow. Because these variables may have direct effects on investment, we add the levels of PPS, ln(MVE), and TENURE to Equation (1). Aggarwal and Samwick (1999) predict a positive relation between investment and the level of PPS. Size and tenure in office are often interpreted as measures of the severity of agency and/or asymmetric information problems, so they might also have direct effects on investment.

We also consider the possibility that the relation between investment and incentives is nonlinear. The rationale for a nonlinear relation is that increasing PPS initially increases the alignment of managers' and shareholders' interests, but managers with very high PPS levels may be entrenched because of their high levels of ownership.

We use a quadratic specification to test for nonlinearity. To test for nonlinearity for the level of PPS, we add PPS2 to the expanded model. To test for nonlinearity for PPS interacted with cash flow, we add PPS2*(CF/K).

The advantage of the expanded model is that it controls for potentially important variables omitted from the more parsimonious Equation (1). The cost is additional multicollinearity, which tends to deflate regression t-statistics. (We note that many pairs of explanatory



64 Financial Management * Summer 2004 variables used in this study are significantly correlated at a level of 0.05 or lower.) For instance, the correlation between PPS and PPS2 is 0.9, and the correlation between PPSo(CF/ K) and PPS is 0.34. These correlations suggest that only the most robust incentive relations will be significant in the expanded model.

Table V reports the results from estimating the expanded model. The additional independent variables are rarely statistically significant, and when we compare them with the results in Table III we see that the adjusted R2 increases only for the LOW Q subsample.

Our primary empirical result, the significant, negative coefficient on PPS*(CF/K) in the LOW Q subsample, is robust to inclusion of the new variables. This result provides further support for the agency cost explanation of the sensitivity of investment to cash flow. However, the new variable, PPS2*(CF/K), is also significant and positive for the LOW Q subsample. Thus, the estimated relation between incentives and investment-cash flow sensitivity appears to be nonlinear for LOW Q firms. The estimated nonlinear relation supports the conclusion that increasing PPS initially increases the alignment of managers' and shareholders' interests, but that very high levels of PPS indicate entrenchment effects.

A second source of the small increase in adjusted R2 for LOW Q firms is a significant, positive coefficient on ln(MVE). This coefficient indicates that larger, LOW Q firms tend to invest more than do smaller, LOW Q firms.

B. Alternative Empirical Specifications with Continuous Measures of Information Asymmetry

Our results so far are based on a dichotomous measure of information asymmetry, the presence or absence of rated commercial paper. Because these results suggest little, if any, role for information asymmetry in the determination of investment - cash flow sensitivities, we incorporate continuous measures of information asymmetry into our model of investment. Both measures are based on data drawn from the I/B/E/S database.

The first measure, NUMANAL, is the number of analysts that followed a firm in the first quarter of the fiscal year over which we measure investment. This measure should be inversely related to the amount of information asymmetry. The second measure, ABSURP, is the absolute value of the percent earnings surprise for the first quarter, where we define the earnings surprise as the difference between realized earnings and the consensus analyst forecast. This measure should be positively related to the amount of information asymmetry.

We also use, but do not report results for, two alternative definitions of the absolute earnings surprise. First, we experiment with truncating this variable to reduce the potential influence of outliers. Second, we scale the earnings surprise by dividing by stock price rather than the earnings per share forecast. Neither alteration has any impact on the interpretation of our results.

Rather than subdividing samples based on commercial paper ratings, we include each variable in our regressions both alone and interacted with cash flow. Sample sizes are reduced slightly by the additional data requirement.

Table VI reports results for the expanded model with continuous controls for information asymmetry. Both the significant, negative coefficient on PPS*(CF/K) in the LOW Q subsample and the significant, positive coefficient on PPS2*(CF/K) are robust to this change in specification.

The continuous information variables are generally not significant and confirm our earlier conclusion that information asymmetry plays almost no role in the determination of investment in the 1994-1998 time period. Neither of the new interaction variables, NUMANAL*(CF/K) nor ABSURP*(CF/K), is statistically significant. Thus, contrary to the primary prediction of




Table V. Results for Expanded Model

This table reports the estimated coefficients from regressing investment to capital, I/K, on various explanatory variables. We base sample divisions on Q and on commercial paper ratings. We base the division by Q on each firm's five-year average value of Q. If that five-year average Q is below the median for all sample firms, then we classify the firm as LOW Q. Otherwise, we classify it as HIGH Q. We classify a firm as RATED if it has rated commercial paper during the five-year sample period, otherwise as UNRATED. All variables are defined in Table I. Q, CASH, K, MVE, TENURE, and PPS are for the beginning of period (prior year-end). All other variables are for year-end. We estimate but do not report year and firm fixed effects. We base t-statistics (in parentheses) on heteroskedasticity- consistent standard errors. The null hypothesis for t-statistics is that the coefficient does not differ significantly from zero. We estimate each regression separately.

Explanatory LOW Q HIGH Q UNRATED RATED Variable Subsample Subsample Subsample Subsample S/K .0356 .0563 .0340 .0396

(4.81)*** (8.46)*** (6.24)*** (6.71)*** Q .0647 .0479 .0684 .0024

(1.47) (2.77)*** (3.56)*** (0.25) CASH/K .3774 .5018 .7234 -.0823

(3.03)*** (2.79)*** (4.47)*** (-1.15) CF/K .3789 .0751 .1709 -.0133

(1.79)* (0.96) (1.87)* (-0.13) PPS*(CF/K) -.0024 .0004 -.0007 -.0008

(-3.00)*** (0.40) (-1.07) (-1.02) PPS2*(CF/K) .000004 .000002 .0000006 .000008

(2.62)*** (0.59) (0.38) (2.35)** ln(MVE).(CF/K) -.0533 -.0233 -.0345 .0019

(-1.92)* (-1.48) (-1.88)* (0.17) TENURE*(CF/K) .0102 .0089 .0142 .0030

(3.00)*** (2.01)** (2.63)*** (1.22) PPS -.0002 .0007 -.0007 .0009

(-0.51) (0.61) (-1.08) (1.03) PPS2 -.0000004 -.000004 .000001 -.000005

(-0.30) (-1.15) (0.96) (-1.49) In(MVE) .0528 .0126 .0188 .0392

(2.33)* (0.33) (0.82) (1.58) TENURE -.0024 -.0018 -.0029 -.0014

(-1.60) (-0.94) (-1.41) (-1.17) Adjusted R2 .61 .58 .55 .65 N 955 955 1265 645


financial constraints studies, information asymmetry appears to play no role in determining the reinvestment of cash flow.

The only significant coefficient for the levels of the information variables, is a negative coefficient on NUMANAL for HIGH Q firms. This coefficient suggests that for HIGH Q firms, firms with high levels of information asymmetry tend to invest more than do firms with low levels of information asymmetry. Even this significant coefficient is contrary to the financial constraints literature, which suggests that information asymmetry should be associated with lower levels of investment.




Table VI. Expanded Model with Continuous Measures of Information Asymmetry

This table reports results using continuous measures of information asymmetry. The new information variables are the number of analysts that follow the firm in a given firm-year (NUMANAL) and the absolute value of the earnings surprise in a given firm-year (ABSSURP). All other variables are defined in prior tables. We base t-statistics (in parentheses) on heteroskedasticity-consistent standard errors. The null hypothesis for t-statistics is that the coefficient does not differ significantly from zero. We estimate each regression separately.

Explanatory LOW Q HIGH Q LOW Q HIGH Q Variable Subsample Subsample Subsample Subsample S/K .0476 .0551 .0470 .0559

(6.20)*** (7.23)*** (5.92)*** (7.18)*** Q .0565 .0432 .0475 .0447

(1.44) (2.41)** (1.20) (2.47) CASH/K .2847 .5165 .2860 .5386

(2.08)*** (3.04)*** (2.13)** (3.16)*** CF/K .3743 .0746 .3171 .0706

(1.58) (0.72) (1.28) (0.81) PPSo(CF/K) -.0031 .0004 -.0032 .0003

(-3.02)*** (0.35) (-3.40) (0.31) PPS2*(CF/K) .000005 .000002 .000005 .000002

(2.60)*** (0.66) (2.90)*** (0.70) ln(MVE)*(CF/K) -.0639 -.0236 -.0486 -.0221

(-2.06)* (-1.12) (-1.50) (-1.27) TENUREo(CF/K) .0157 .0092 .0153 .0092

(3.97)*** (1.94)* (3.96)*** (1.90)* PPS -.0005 .0008 -.0004 .0008

(-1.03) (0.75) (-0.66) (0.74) PPS2 .0000 -.000005 -.0000004 -.000005

(0.00) (-1.27) (-0.21) (-1.27) In(MVE) .0497 .0249 .0560 .0096

(1.78)* (0.64) (2.07) (0.25) TENURE -.0027 -.0021 -.0026 -.0019

(-1.63) (-1.02) (-1.60) (-0.93) NUMANAL .0051 -.0069

(1.62) (-1.98)** NUMANAL*(CF/K) .0045 .0004

(0.93) (0.09) ABSURP -.00006 -.00003

(-0.86) (-0.65) ABSURPo(CF/K) .000006 -.00003

(0.10) (-0.72) Adjusted R2 .64 .56 .64 .56 N 896 929 891 927

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

*Significant at the 0.10 level.

C. Endogeneity Concerns

An implicit assumption of our empirical tests is that incentives are exogenously set, so that predetermined levels of PPS determine investment. Although this assumption is used often in empirical studies, it is possible that investment and incentives are jointly determined




to reflect the severity of agency or asymmetric information problems. The possibility of

endogenously determined investment and incentives provides an alternative set of

hypotheses that lead to a different interpretation of estimated coefficients.

Suppose that there is a free cash flow problem and that incentives are optimally set to minimize the severity of this problem. We can expect that firms that experience a positive shock to free cash flow will increase investment and that shareholders choose high levels of PPS to reduce the severity of the free cash flow problem. These firms would show up in the data with

high investment-cash flow sensitivities and high PPS, so that the estimated coefficient on the PPS variable interacted with cash flow would be positive. Under this alternative hypothesis, our empirical finding that ao is not positive implies either that there is no free cash flow problem or that incentives are not endogenously set to address this problem.

Suppose that incentives are optimally set to minimize the severity of asymmetric information

problems. We assume that there are two types of firms, high quality and low quality. The market can observe the quality of RATED firms from the information disclosed in the rating process. Since there is no information asymmetry for RATED firms, investment is not sensitive to cash flow and a5 is predicted to be zero. For UNRATED firms, quality is unobservable and the lemon's problem associated with external finance causes the investment of high quality firms to be sensitive to cash flow. Thus, shareholders of high quality UNRATED firms should choose a high PPS to deter their managers from selling underpriced equity. On the other hand, the shareholders of low quality UNRATED firms are indifferent to the choice of PPS, because investment for these firms is at the efficient level. Since high quality UNRATED firms have both higher PPS and higher investment-cash flow sensitivities than do low quality UNRATED firms, we predict a positive a5 for UNRATED firms. Since the estimated a5 for UNRATED firms is negative, this alternative explanation is not supported.

Suppose that incentives are optimally set to minimize the severity of the shirking problem. Firms with shirking managers invest less available cash flow than does the average firm, and have lower investment-cash flow sensitivities. Because shirking managers pass up valuable investment opportunities, Q will be lower than it would be under optimal investment, and shareholders will increase PPS to induce managers to invest more. In this case, endogenously determined investment and incentives predict that we should observe low investment-cash flow sensitivities and high PPS for firms with lower than average Q. Our empirical finding of a significant, negative ac5 for LOW Q firms is consistent with this alternative interpretation.

VI. Conclusions

Viewed in the context of the recent literature, our findings indicate that the influence of managerial incentives on investment is multifaceted. Hadlock (1998) finds that strengthening the alignment of managers' and shareholders' interests increases the severity of financial constraints due to information asymmetry. Aggarwal and Samwick (1999) find evidence that stronger alignment of managers' and shareholders' interests reduces the agency costs of managerial shirking. Both studies reject the hypothesis that equity based incentives alleviate the agency costs of free cash flow. We find that the sensitivity of investment to cash flow is reduced as PPS increases. Therefore, we conclude that the dominant influence of increasing alignment in our sample is a reduction in the agency costs of free cash flow. The most likely reason that our finding differs from Hadlock's finding is that our sample is from a period of stronger economic conditions when firms are more likely to generate excess cash and less likely to be financially constrained. The most likely reason that our finding differs from that




ofAggarwal and Samwick is that we provide a direct test of the influence of incentives on the tendency to invest cash flow.

Our results should be interpreted with concerns about the possibility of endogenously determined incentives and investment in mind. However, our main message is that incentives have many influences on the firm's investment decision, and that the influence identified by empirical work is sensitive to both the sample period and the test design.E

Appendix A. Estimation of Pay-Performance-Sensitivity

We obtain all data from Standard and Poor's ExecuComp data file. We estimate PPS for each CEO at each fiscal year-end from 1993-1997. Since this research focuses on incentives for chief executive officers (CEOs), we choose firms only if ExecuComp contains valid CEO information. A single executive must be identified as the CEO at fiscal year-end, based on the variables BECAMECEO and LEFTOFC that indicate the beginning and ending dates of the CEO's tenure in office. (For clarity, all ExecuComp variable names are italicized and capitalized throughout this Appendix.)

Stock PPS estimates the dollar change in CEO holdings of common stock per $1,000 change in the market value of stock outstanding. We calculate Stock PPS by multiplying 1,000 by the ratio of shares owned by the CEO, SHROWN, to the total number of common shares outstanding, SHRSOUT. If the flag variable, PCINCLOPT, is "TRUE," the number of shares owned includes stock options. In this case we reduce the number of shares owned by the reported aggregate number of options held by the CEO at year-end (UEXNUMEX + UEXNUMUN).

ExecuComp reports both detailed information on current year option grants and aggregate information on year-end option holdings. Aggregate information is limited to in-the-money grants, so an options PPS based only on aggregate grants is likely to understate options PPS. Because our sample period is one of rising stock prices, and most options are granted at-the-money, the degree of understatement is not likely to be severe in our sample. However, as a partial adjustment for this problem, we also incorporate into our PPS estimate current- year grants that we identify as out-of-the-money at year-end. For each executive and fiscal year-end, we calculate total options PPS as the sum of aggregate options PPS and out-of- the-money grant PPS.

For a given option grant, we calculate the option PPS as the options "delta" (the partial derivative of the option price with respect to the stock price) multplied by the number of options multiplied by 1,000. The delta is N(d,) from the Black-Scholes (1973) option pricing model as adjusted by Merton (1973) to incorporate continuous dividends. We calculate

N(d,) as follows:

N(d = N(In(S / X)+ (r - d + o' /

2)TT (Al)

where: N(*) = cumulative probability function for the normal distribution, S = the fiscal year-end price of the underlying stock, denoted PRCCF in ExecuComp, X = the exercise price of the option, denoted EXPRIC in ExecuComp,




r = yield for a seven-years-to-maturity US Treasury Bond, denoted RISK_FREE_RATE, d = expected dividend yield, denoted BS_YIELD in ExecuComp, a2 = stock return volatility for prior 60 months, denoted BS_ VOLATILITY, and T = time to maturity of the option, calculated as the difference between the option's

expiration date (EXDATE) and the date of the fiscal year-end.

For each executive and fiscal year-end we sum the PPS estimates for the individual out-of- the money grants to calculate total out-of-the money grant PPS.

The calculation of options PPS for aggregate year-end grants requires modifications to the above procedure. ExecuComp does not report the time to expiration for aggregate grants, so we assume a five-year term to maturity. Exercise prices are not reported for aggregate grants and must be implied from the reported intrinsic values of the executive's option holdings:

( INMONEX + INMONUN ( UEXNUMEX + UEXNUMUN (A2)

where: INMONEX= intrinsic value of in-the-money unexercised exercisable options, INMONUN = intrinsic value of in-the-money unexercised unexercisable options, UEXNUMEX= total number of in-the-money unexercised exercisable options, and UEXNUMUN = total number of in-the-money unexercised unexercisable options.

Appendix B. Variable Definitions

We obtain non-compensation data other than TENURE from the Standard & Poor's Compustat files. Definitions and Compustat variable numbers are as follows:

I= Capital Expenditures = Item 30, S= Sales = Item 12, CF = Cash Flow = Income Before Extraordinary Items + Depreciation and Amortization -

Preferred Dividends - Common Dividend = Item 18 + Item 14 - Item 19 - Item 21, CASH= Cash and Short-Term Investments = Item 1, K = Net Property, Plant and Equipment = Item 8, Q = Chung and Pruitt's (1994) Approximate Q = (Market Value of Equity + Liquidating

Value of Preferred Stock + Total Long-Term Debt + Net Short-Term Debt)/Total Assets = (Item 199 x Item 25 + Item 10 + Item 9 + (Item 5-Item 4))/Item 6, and

MVE = Market Value of Equity = Item 199 x Item 25.

We follow Kaplan and Zingales (1997) in using a simple approximation of Q, rather than more complicated specifications. Results reported in Chung and Pruitt (1994) and Perfect and Wiles (1994) indicate that little is to be gained by using more elaborate specifications. Compustat item 283 provides Standard and Poor's commercial paper ratings.




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Issue Table of ContentsFinancial Management, Vol. 33, No. 2 (Summer, 2004), pp. 1-132Front Matter [pp. 1-50]Does Diversification Cause the "Diversification Discount"? [pp. 5-27]Imperfect Competition, Debt, and Exit [pp. 29-49]CEO Incentives, Cash Flow, and Investment [pp. 51-70]Excess Cash Flows and Diversification Discount [pp. 71-88]Bank Relationships and Their Effects on Firm Performance around the Asian Financial Crisis: Evidence from Taiwan [pp. 89-112]Back Matter [pp. 113-132]

ceo incentives, cash flow, and investment

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