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Executive compensation and risk: The case of internet firms
Journal of Corporate Finance 12 (2005) 80 – 96 www.elsevier.com/locate/econbase
Executive compensation and risk: The case of internet firms Carol Callaway Deea,*, Ayalew Lulsegeda, Tanya S. Nowlinb a
College of Business, Florida State University, Tallahassee, FL, 32306-1110, United States b University of Louisiana at Lafayette, United States
Received 30 November 2004; received in revised form 21 December 2004; accepted 26 December 2004 Available online 22 June 2005
Abstract A major prediction of agency theory is that there is a trade-off between risk and incentive compensation. Aggarwal and Samwick (1999) [Aggarwal, R., Samwick, A., 1999. The other side of the trade-off: the impact of risk on executive compensation. Journal of Political Economy, 107, 65– 105.] directly test and find results consistent with agency theory—pay-performance sensitivity is decreasing in risk. However, Prendergast (2002, 2000) [Prendergast, C. 2002. The tenuous trade-off between risk and incentives. Journal of Political Economy 110 (5), 1071–1102; Prendergast, C. 2000. What trade-off risk and incentives? The American Economic Review 90 (2), 421–425.] offers a number of reasons why the sensitivity of pay to performance can be higher in risky environments. We use data from a sample of Internet firms for 1997–1999 to provide empirical evidence on these competing arguments regarding the relation between risk and CEO compensation. Consistent with Aggarwal and Samwick (1999), our results show that pay–performance sensitivity declines with increases in variance in a base model. After controlling for size, we find that pay–performance sensitivity is positively related to risk, consistent with the theoretical predictions in Prendergast (2002, 2000). However, sensitivity tests in later periods show that the Aggarwal and Samwick (1999) results are more robust to changes in the economic environment. D 2005 Elsevier B.V. All rights reserved. JEL classification: M41; J33 Keywords: Executive compensation; Risk; Internet; New economy
* Corresponding author. Tel.: +1 850 644 7879; fax: +1 850 644 8234. E-mail address: [email protected] (C.C. Dee). 0929-1199/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jcorpfin.2004.12.002
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1. Introduction One of the main predictions of agency theory is that there is a trade-off between incentive compensation and risk. Agents must be compensated for bearing additional risk, resulting in higher wage costs. Thus, firms must trade off higher incentives against higher wage costs, which can lead to a reduction of incentive-based compensation as risk increases. Using a sample of large firms from the ExecuComp database, Aggarwal and Samwick (1999, hereafter AS 1999) directly test and find results consistent with the theory—pay–performance sensitivity is decreasing in risk. Jin (2002) also finds a negative relation between risk and pay–performance sensitivity in an augmented model controlling for variables that capture the value of CEO effort. Despite these recent findings, however, overall empirical evidence on the effect of risk on pay–performance sensitivity is mixed.1 Motivated by the mixed empirical findings, and the apparent inconsistency between the predictions of standard agency theory and anecdotal evidence, Prendergast (2002) proposes (but does not empirically test) an alternate theory. Prendergast (2002, 2000) offers a number of reasons why the sensitivity of pay to performance can be higher in risky environments. For example, he notes that in uncertain environments, it is more difficult for the principal to know on what projects the agent should be working. Thus, in riskier settings, it is less costly to reward the agent based on observed output (as opposed to monitoring his inputs), leading to a positive relation between pay–performance sensitivity and risk.2 In particular, Prendergast (2002, fn. 1) notes that the use of options to compensate employees in high-tech industries seems unlikely if the main determinant of pay–performance sensitivity is the trade-off of risk and incentives. In this paper we shed light on this debate by providing empirical evidence on the relation between pay–performance sensitivity and risk in internet firms—firms which are inherently high risk. Our study contributes to the existing literature in at least two ways. First, we document whether the principal–agent theory prediction of a negative association between pay–performance sensitivity and risk that AS (1999) find in a sample of large firms will also hold in inherently high-risk internet firms. Such evidence goes beyond what Aggarwal and Samwick showed, and is particularly interesting given the argument by Prendergast (2002, 2000) that one should not find a negative relation between incentives and risk in high-tech firms. Second, we provide evidence on the robustness of the findings in AS (1999) to the inclusion of additional control variables (such as size, growth, ownership, etc.) and to changes in the macro economic environment across time (1997– 1999 and 2000–2002). Internet firms offer a unique setting in which to examine the conflicting predictions on the relation between compensation and risk.3 Typically, Internet firms have more
1
Prendergast (2002, p. 1077) offers a summary of this research. Note that Prendergast (2002, 2000) does not suggest that a positive relation between risk and incentive compensation should exist, merely that credible reasons exist why that may be the case. 3 We define an internet firm as one bthat would not exist if it were not for the internet, and for which 51% or more of its revenue comes from or because of the internetQ (Hand, 2000, p. 2). 2
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intangible assets than assets in place, are high growth, and highly volatile.4 Consequently, there is a high degree of information asymmetry between the CEO and shareholders. High information asymmetry implies greater monitoring difficulty (based on inputs). Other things being equal, greater monitoring difficulty leads to a higher demand for performance-based compensation in order to motivate the CEO to select projects consistent with the maximization of shareholder wealth (Jensen and Meckling, 1976; Smith and Watts, 1992; Lulseged and Christie, 2002; Prendergast, 2002). This suggests a positive relation between compensation and risk. However, tying compensation to firms’ market performance shifts risk away from welldiversified shareholders onto executives holding undiversified portfolios, and may result in inefficient risk sharing. In high risk Internet firms, tying compensation to market performance can be very costly and even result in a decrease in shareholder value for several reasons. First, CEOs demand a premium for bearing the additional risk imposed on them by the compensation mix, and this cost increases with risk, particularly risk over which the CEO has no control (Core et al., 1999; Meulbroek, 2001; Jin, 2002; Lulseged and Christie, 2002; Nowlin and Christie, 2002). Second, CEOs will take actions to diversify their portfolio such as exercising stock options and selling the acquired shares. These acts of diversification by the CEO not only decrease the incentive alignment goal of the compensation, but also may reduce firm value if the market interprets the insider sale as a negative signal (Meulbroek, 2000). Third, Meulbroek (2001) finds that there is a dead weight loss (increasing with risk) associated with performance-based compensation. She notes that because the employee is undiversified, he or she values incentive stock options at an amount less than what the firm could obtain by selling the securities in the open market to a diversified investor. These reasons, consistent with the trade-off of risk and incentives in the traditional agency model, suggest that incentive-based compensation should decline with increases in risk. Given these competing arguments and conflicting predictions, we use data from a sample of Internet firms for 1997–1999 in order to provide empirical evidence on the issue. Our study extends the work of AS (1999) and offers evidence on the conjectures of Prendergast (2002, 2000) regarding the relation between risk and incentive-based compensation for CEOs in uncertain and rapidly changing technological environments. Our empirical models are based on AS (1999). Their model is based on principal–agent theory and lends itself to extensions that allow us to examine the relation between compensation and additional firm-specific characteristics. As in AS (1999), who examine a broad sample of large public companies, our results show that pay–performance sensitivity declines with increases in risk in a base model that excludes other control variables. After adding firm size (net sales) to the base model, results show that pay– performance sensitivity is positively related to risk, consistent with the claim of Prendergast (2002, 2000) that, in high-tech environments, the need for increased incentive
4 Our sample of internet firms differs in several respects from large, established firms typically studied in compensation research. For example, our firms are high growth, with high CEO percentage ownership and little debt. We discuss this more in Section 3.
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compensation due to monitoring difficulty outweighs the trade-off of risk and incentives from the traditional agency model. Our sample period is from 1997 to 1999, a period during which internet firms were experiencing rapid growth, elevated stock valuations, and high price uncertainty. This raises the question as to what can be learned about fundamental finance relationships by examining internet firms during this period, and what the implications are for today.5 To test the robustness of our results to changes in the environment in which internet firms operate, we repeat our analyses using data on our sample firms for the period 2000 to 2002. Consistent with our results for the base model, we find that compensation is positively related to returns, while pay–performance sensitivity declines with increases in the variance of dollar returns. Unlike our results for the earlier time period, however, when we add size to the model, compensation remains positively related to returns while pay– performance sensitivity still decreases with increases in risk. This finding for the later period, that pay–performance sensitivity declines with increases in risk even after controlling for size suggests that the AS (1999) result is more robust to changes in the economic environment. The next section presents the empirical model, and theory and prior research that motivates our research design. Section 3 presents empirical analyses, and the final section summarizes findings and conclusions.
2. Empirical model We estimate our models in three stages. First, we estimate a base model similar to AS (1999). Second, we add a control variable representing firm size (net sales) to the base model. Finally, as a robustness check, we augment the base model with size and other control variables representing firm characteristics that prior research finds to have an effect on total compensation and pay–performance sensitivity. 2.1. The base model In the base model, we regress compensation on dollar returns, the variance of dollar returns, the product of dollar returns and return variance, and year effect dummy variables. COMP ¼ b0 þ b1 RETURNS þ b2 VAR þ b3 RET*VAR þ b4 D1998 þ b5 D1999 þ e
ð1Þ
COMP is total flow compensation, i.e., the sum of cash compensation (salary and bonus) plus the value of options granted during the year. We collect compensation variables (including those needed to value options) from proxy statements available from either LexisNexis Academic Universe or the Securities and Exchange Commission’s
5
We thank the editor for this point.
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(SEC) Edgar website. We value stock options using the Black and Scholes (1973) optionpricing model modified for early exercise, with a risk-free rate of 6%.6 Consistent with AS (1999), we use annual dollar stock returns to shareholders, RETURNS, as our performance measure. To calculate RETURNS, we compute annual percentage returns from daily CRSP returns and multiply the percentage return by beginning market value (AS 1999). As in AS (1999), we use the variance of dollar returns as a proxy for risk. VAR is the cumulative distribution function (CDF) of the variance of dollar stock returns. To calculate VAR, we rank variances of the dollar stock returns for each firm, and then transform the ranks so that they lie uniformly between zero and one. RET*VAR is the product of RETURNS and VAR. D1998 and D1999 are year dummies for 1998 and 1999, included to control for possible year effects such as changes in the macroeconomic environment; e is the error term. 2.2. Model controlling for size A size proxy is added to the base model as an additional explanatory variable. Size can affect pay–performance sensitivity for several reasons. Size may be a proxy for risk (Jensen and Murphy, 1990; Aggarwal and Samwick, 2002, 2003), CEO wealth constraints (Demsetz and Lehn, 1985; Core and Guay, 2002), or the marginal product of CEO effort (Core et al., 1999; Smith and Watts, 1992; Gaver and Gaver, 1993). As in Jin (2002) and Aggarwal and Samwick (2003, 2002), we use sales as a proxy for size. COMP ¼ b0 þ b1 RETURNS þ b2 VAR þ b3 RET*VAR þ b4 D1998 þ b5 D1999 þ b6 SALES þ b7 RET*SALES þ e
ð2Þ
SALES is the CDF of net sales and all other variables are as defined before. The RET*SALES interaction term captures the effect of size on pay–performance sensitivity. 2.3. Model controlling for size and other firm characteristics Prior research documents that compensation is related to the underlying characteristics of the firm and the skill level of the CEO (Core et al., 1999; Smith and Watts, 1992; Gaver and Gaver, 1993). AS (1999) include a CEO fixed effect variable in their model. This variable is presumed to capture the effects of the underlying characteristics of the firm and the CEO’s skill level because in equilibrium firms are expected to hire CEOs with the skill level demanded by the job.
6 We use a term to expiration of four years because an examination of the proxy statements indicates that the average vesting period for stock option grants is 4 years. Firms in our sample have not paid out any dividends, so returns cum dividends are equal to returns ex dividends. For the firms with sufficient past return data, we use a maximum of 60 months of pre-1997 stock returns to compute the standard deviation of returns. Three firms have 60 months of historical returns data. For the remaining firms, the standard deviation calculation uses all available monthly returns (both pre-and post-compensation years) ending with December 1999. The actual number of months used in the calculation ranges from 25 to 60 per firm.
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We do not include CEO fixed effects in our model because of the reduction in degrees of freedom that would result. Instead, we include proxies for the underlying characteristics of firms that prior research finds to be related to compensation. Consistent with prior literature, we expect proxies for the underlying characteristics of the firm to capture the cross-sectional variation in the demand for CEO skill and the variation in CEO marginal productivity across firms. Similar to Smith and Watts (1992) and Gaver and Gaver (1993), we include proxies for growth and leverage in our extended model. We also include CEO ownership percentage as an explanatory variable. Our extended model is: COMP ¼ b0 þ b1 RETURNS þ b2 VAR þ b3 RET*VAR þ b4 D1998 þ b5 D1999 þ b6 SALES þ b7 RET*SALES þ b8 GROWTH þ b9 RET*GROWTH þ b10 OWN þ b11 RET*OWN þ b12 LEV þ b13 RET*LEV þ e
ð3Þ
GROWTH is the CDF of the market to book ratio: market value of equity plus book value of debt, divided by book value of assets (Smith and Watts, 1992). OWN is the CDF of the percentage ownership of the CEO. LEV is a proxy for leverage, and equals the CDF of the debt to equity ratio. All other variables are as previously defined. The variables GROWTH, OWN, and LEV also enter the regression multiplicatively with RETURNS (RET*GROWTH, RET*OWN, and RET*LEV). The interaction variables are included to control for the effects of size, growth, CEO ownership percentage, and leverage on the sensitivity of pay to performance. Our predictions for the effects of these variables on pay–performance sensitivity are based on their relation to incentive-based compensation. In general, because incentive-based compensation is more sensitive to firm performance than is fixed salary, a variable that is associated with increases (decreases) in incentive-based compensation will also be associated with increases (decreases) in pay–performance sensitivity. The problem of monitoring managers increases with both growth opportunities and firm size (Prendergast, 2002, 2000). Larger firms tend to be decentralized because of span-of control issues, thus exacerbating the monitoring problem (Eaton and Rosen, 1983; Christie et al., 2003). Additionally, the marginal productivity of CEO effort is higher for growth firms and large firms (Smith and Watts, 1992; Gaver and Gaver, 1993; Baker and Hall, 2004). For these reasons, growth firms and larger firms are more likely to use incentivebased compensation than are other firms (Smith and Watts, 1992; Gaver and Gaver, 1993). This would lead to a positive sign prediction on RET*SALES and RET*GROWTH. However, growth and size may be proxies for risk (Jensen and Murphy, 1990; Aggarwal and Samwick, 2002, 2003). Thus, if there is a tradeoff between risk and incentives as predicted by agency theory, pay–performance sensitivity may decline with increases in growth and size. Additionally, size may be negatively related to payperformance sensitivity due to CEO wealth constraint and risk aversion effects (Larner, 1966; Demsetz and Lehn, 1985; Core and Guay, 2002). These reasons would lead to negative sign predictions on RET*SALES and RET*GROWTH. Given these opposing predictions, the relation between pay–performance sensitivity and growth and size is an empirical issue. We make no sign prediction on SALES or
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GROWTH, because it is not clear whether size or growth are related to compensation after controlling for their effects on pay–performance sensitivity. Prior research finds that firms with high CEO ownership use incentive-based compensation less (Mehran, 1995; Agrawal and Knoeber, 1996; Core et al., 1999; Lulseged and Christie, 2002). Additionally, studies show that highly leveraged firms pay lower total compensation and use incentive-based compensation less in order to minimize managers’ incentives to increase the wealth of shareholders at the expense of debtholders (Smith and Watts, 1992; Gaver and Gaver, 1993; Agrawal and Knoeber, 1996; Lulseged and Christie, 2002). Therefore, we predict that the sensitivity of pay to market performance will decrease with both CEO ownership and leverage, because decreases in the amount of incentive-based compensation are associated with decreases in pay– performance sensitivity. That is, we predict a negative sign on both RET*OWN and RET*LEV. Because it is unclear whether CEO ownership or leverage is related to compensation after controlling for their effects on pay–performance sensitivity, we make no sign prediction on OWN or LEV.
3. Empirical analysis We present and discuss sample selection and descriptive statistics in Section 3.1, correlations among selected variables of interest in Section 3.2, regression results in Section 3.3, and robustness tests in Section 3.4. 3.1. Sample selection and descriptive statistics Our sample is selected from the population of 279 Internet firms listed on the bInternetStockListQ at May 6, 2000. This list is compiled by internet.com Corporation and has been used by prior researchers in selecting a sample of Internet firms (Hand, 2001, 2000; Trueman et al., 2000). The InternetStockList consists of all 50 firms included in the Internet stock index (ISDEX), along with many smaller Internet firms.7 In order to obtain sufficient post-IPO price data, financial data, and proxy statements, we limit our initial sample to firms that went public prior to January 1, 1998. There were 55 firms on the InternetStockList that met this criterion. We download proxy statements from either LexisNexis Academic Universe or the SEC’s Edgar database. Financial variables are from Standard & Poor’s Research Insight (Compustat) database. We omit firm-years for which (1) proxies are not available, (2) proxies do not contain enough compensation data needed for our tests, or (3) financial data is not available on Compustat. We exclude any years in which the CEO changed, or was not in office for the entire year. We delete one highly influential observation based upon our analysis of studentized residuals, DFFITS, and DFBETAS (Belsley et al., 1980).8 7
The ISDEX (a subset of the InternetStockList) is maintained by internet.com Corporation, and ISDEX futures are traded on the Kansas City Board of Trade. At February 12, 1999, the ISDEX represented 95% of the market capitalization of publicly-traded internet stocks (Chadwick Investment Group, 2004). 8 The 1999 observation for Broadvision was deleted.
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Table 1 Firms in final sample Name
Ticker
Name
Ticker
Amazon.com Inc America Online Inc At Home Corp AXENT Technologies Bluefly Broadvision Inc Cisco Systems CMGI Inc C/NET Concentric Network Crosswalk.Com CyberCash Cylink EarthLink Network eFax Egghead.com E*TRADE Group fine.com International Go2Net HomeCom Communications IDT Infonautics InterVU
AMZN AOL ATHM AXNT BFLY BVSN CSCO CMGI CNET CNCX AMEN CYCH CYLK ELNK EFAX EGGS EGRP FDOT GNET HCOM IDTC INFO ITVU
Lycos Inc NAVIDEC NetSpeak Network Associates Network Solutions Inc Open Market Peapod Preview Travel PSINet RealNetworks Inc RMI.NET Rogue Wave Software RSA Security Security First Technologies SportsLine USA Spyglass THINK New Ideas USWeb/CKS Visual Data V-ONE Voxware White Pine Software Yahoo!
LCOS NVDC NSPK NETA NSOL OMKT PPOD PTVL PSIX RNWK RMII RWAV RSAS SONE SPLN SPYG THNK USWB VDAT VONE VOXW WPNE YHOO
Table 1 lists the final sample, which consists of 46 separate firms with 104 firm-years of data.9 Note that, by design, our final sample differs from that of AS (1999). Their sample is selected from the ExecuComp database, which includes primarily large, well-established companies operating in a variety of industries. Our purpose, however, is to extend AS (1999) and empirically test the theoretical claims of Prendergast (2000, 2002) that predictions of the traditional agency model regarding risk and incentives may not hold for high-tech firms. Thus, we specifically focus on Internet firms in order to provide empirical evidence regarding the untested theory of Prendergast (2000, 2002). From each proxy statement, we gather compensation data (cash compensation and options granted) and CEO percentage ownership. We value the options granted using the Black–Scholes option pricing formula modified for early exercise, as described more fully in Section 2.1. Table 2 presents descriptive statistics. The sample firms have wide variability in size. Net sales range from $193,000 to $12.154 billion, with mean (median) values of $456.6 million ($49.3 million). The firms in the sample are, on average, larger than the IPO firms in Nowlin and Christie (2002), who report mean (median) sales of $53 million ($22
9
The tenor of our conclusions does not change when we conduct our analyses using only firms that have complete data for all three years.
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Table 2 Descriptive statistics (n = 104) Minimum
Mean
Median
Maximum
Std. dev.
Firm characteristics Sales* 0.193 456.614 49.300 12,154.000 1641.520 Net Income* 1457.640 15.901 8.801 2096.000 334.753 Total assets* 3.099 779.909 81.082 14,725.000 2079.890 Beginning market value* 5.230 2883.129 167.927 97,512.470 11,707.036 Return on assets 2.399 0.282 0.207 0.819 0.419 Annual market return 0.602 1.505 0.508 16.267 2.650 Annual market return (dollars)* 1094.470 3716.75 46.034 92,292.98 13,693.090 Standard deviation of market return 0.101 0.281 0.280 0.489 0.064 CDF of variance of dollar returns 0.006 0.500 0.500 0.986 0.289 Dollar returns*CDF of variance of dollar returns 0.899 3.477 0.016 91.036 13.235 Leverage (debt to equity) 0 0.075 0.004 0.829 0.167 Growth (market to book) 0.885 6.723 4.153 78.565 9.255 Compensation variables Cash compensation* Total compensation* % option value to total compensation
0.173 0.070 0
0.311 10.670 50.555
0.232 0.745 65.662
1.664 151.294 99.839
0.273 27.012 42.255
Others CEO ownership %
0
10.996
3.780
70.800
13.942
*Millions of dollars.
million). Market values are right-skewed, with a mean value of $2.883 billion and median of $167.9 million. Mean (median) net income is $15.9 million ($ 8.8 million), and mean (median) return on assets is 28.2% ( 20.7%). However, the mean (median) annual return is 150.5% (50.8%). The mean (median) standard deviation of percentage returns is 28.1% (28.0%), indicating the high volatility that characterizes such firms.10 Further, untabulated results show that 76% of the firms have net losses, yet only 31% have negative returns. Negative earnings are typical of Internet firms during our sample period. Hand (2001, 2000) reports negative earnings for 87% of the firms in his sample, while Engel et al. (2002) report negative earnings for 85% of their sample Internet firms during their post-IPO period. The mean (median) ratio of market to book in our sample is 6.7 (4.2). These growth numbers are much higher than those in other studies using larger, more established firms. For example, in a 1993 sample of S&P 500 firms, Lulseged and Christie (2002) report mean and median growth of 1.6 and 1.3, respectively. Not surprisingly, our sample firms have little debt, as evidenced by the low mean (median) debt to equity ratio of 7.5% (0.4%). This is consistent with predictions in agency theory and evidence from prior research that growth firms and firms with high levels of intangible assets have lower leverage. Internet firms are high growth firms; and high 10 For a sample of IPO firms from 1996–1997, Nowlin and Christie (2002) report mean and median standard deviation of returns of 23%. Lulseged and Christie (2002) report a mean (median) standard deviation of returns of 7.5% (7.1%) in a sample of S&P 500 firms for 1993.
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Table 3 Correlation table (N = 104) Variable
MV
MV SALES GROWTH VAR OWN LEV
1 0.819 0.325 0.827 0.330 0.068
SALES (0.001) (0.001) (0.001) (0.001) (0.495)
1 0.235 0.737 0.246 0.174
(0.016) (0.001) (0.012) (0.077)
GROWTH
VAR
OWN
LEV
1 0.465 (0.001) 0.107 (0.280) 0.001 (0.991)
1 0.189 (0.054) 0.108 (0.274)
1 0.285 (0.003)
1
Pearson Correlation coefficients ( p-values in parentheses). MV equals the CDF of the market value of equity at the beginning of the year. SALES is the CDF of net sales. GROWTH is the CDF of the market-to-book ratio (market value of equity plus book value of debt, divided by book value of total assets.) VAR is the CDF of dollar return variance. OWN is the CDF of CEO ownership percentage. LEV equals the CDF of the debt-to-equity ratio.
growth firms tend to have lower debt to equity ratios (Myers, 1977; Jensen, 1986; Smith and Watts, 1992; Gaver and Gaver, 1993). Additionally, most of these firms have had a recent cash infusion resulting from a public offering. In our sample, CEO ownership percentages are high, with a mean (median) of 11% (3.8%). This is comparable to the sample of IPO firms in Nowlin and Christie (2002), who report mean (median) CEO ownership percentages of 17.6% (10.2%), and to the post IPO sample of Internet firms in Engel et al. (2002), who report mean (median) CEO ownership percentages of 10.4% (5.6%). However, as noted by Nowlin and Christie (2002) it is much higher than the typical CEO ownership percentage reported in studies of large, nonInternet firms. For a sample of large firms, Core et al. (1999) report mean (median) CEO ownership percentages of 1.5% (0.09%). The high percentage ownership for our sample firms implies that there may be less need for stock-based compensation for these firms. For the CEOs in our sample, cash compensation (salary plus bonus) is relatively modest compared to total compensation. Mean (median) cash compensation is $311,000 ($232,000). Total compensation, which equals cash compensation plus the value of options granted, is right-skewed, with mean (median) values of $10.7 million ($745,000). Additionally, the median percentage option value to total compensation is 66%. These statistics show that these firms rely heavily on options to compensate their CEOs. 3.2. Correlation analysis Table 3 presents Pearson correlations among selected variables of interest. We find a significantly positive (0.827) correlation between variance of dollar returns (VAR) and market value (MV).11 The correlation between variance of dollar returns (VAR) and SALES, although slightly lower, is significantly positive (0.737). Market value and growth 11 Both Core and Guay (2002) and AS (1999) calculate variance of returns using 60 monthly returns prior to the beginning of the year. We measure variance of returns using 60 monthly returns prior to the beginning of 1997, whenever we have data. When a firm has no or some past history (less than 60 months) prior to the beginning of 1997, we calculate variance of returns using as many monthly returns as possible (at least 21 and at most 60 monthly returns) before and after the beginning of 1997.
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are positively correlated (0.325). This correlation is partly driven by the way we measure the two variables. Growth is measured as the ratio of market value of equity plus the book value of debt to book value of assets. Thus, the numerator in the growth measure includes the market value of equity and hence it is not surprising that we find this positive correlation. Consistent with the prediction of agency theory that firms with high leverage have higher managerial ownership (Jensen and Meckling, 1976), we find a positive correlation of 0.285 between leverage and CEO ownership (OWN). The negative correlation between CEO ownership and market value ( 0.330) is consistent with results in Demsetz and Lehn (1985) and Core and Guay (2002). 3.3. Regression results Table 4 presents regression results. There are two main differences between our models and those of AS (1999). First, as discussed in footnote 6, we calculate the variance of dollar returns somewhat differently than they do because of data limitations. Second, we use ordinary least squares (OLS) regressions rather than median regressions used by AS (1999), although AS (1999) present OLS results as well. AS (1999) note that the precision of parameter estimates is higher in median regressions, because the median is a more
Table 4 Regression results (COMP= b 0 + b 1RETURNS + b 2VAR + b 3RET * VAR + b 4D1998 + b 5D1999 + b 6SALES + b 7 RET * SALES + b 8 GROWTH + b 9 RET * GROWTH + b 10 OWN + b 11 RET * OWN + b 12 LEV + b 13 RET * LEV + e) A* Intercept RETURNS VAR RET*VAR SALES RET*SALES GROWTH RET*GROWTH OWN RET*OWN LEV RET*LEV Adj. R2 Prob. F
2.380 0.021 12.144 0.020
0.576 0.001
B (0.618) (0.001) (0.083) (0.001)
C
4.052 0.009 1.285 0.016 14.037 0.025
0.656 0.001
(0.352) (0.051) (0.885) (0.056) (0.107) (0.001)
4.686 0.028 5.273 0.013 13.284 0.034 1.534 0.007 9.748 0.003 2.930 0.001 0.712 0.001
(0.432) (0.001) (0.568) (0.204) (0.122) (0.001) (0.804) (0.006) (0.098) (0.011) (0.606) (0.346)
p-values are in parentheses. COMP equals cash salary and bonus, plus value of options granted during the year (in millions). RETURNS equals annual percentage return times beginning market value in millions. RET*VAR is an interaction term equal to RETURNS times VAR. D1998 is an indicator variable equal to one if the year is 1998, zero otherwise. D1999 is an indicator variable equal to one if the year is 1999, zero otherwise. SALES is the CDF of net sales. RET*SALES is equal to RETURNS times SALES. GROWTH is the CDF of the market-to-book ratio (market value of equity plus book value of debt, divided by book value of total assets.) RET*GROWTH equals RETURNS times GROWTH. OWN is the CDF of CEO ownership percentage. RET*OWN equals RETURNS times OWN. LEV equals the CDF of the debt-to-equity ratio. RET*LEV equals RETURNS times LEV. *p-values in Column A are calculated using asymptotic standard errors (White 1980). Parameter estimates for both D1998 and D1999 are insignificant in all models, and omitted for brevity.
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robust measure of the center of the data than the mean. Using OLS will thus work against our finding significant results. Column A presents our base model. The results are very similar to those reported by AS (1999; Table 6, p. 88). Compensation is positively associated with returns, and, consistent with the predictions of principal–agent theory, pay–performance sensitivity declines with increases in the variance of dollar returns. Both year dummies (not reported in the table) are insignificant. The model explains about 58% of the variation in total compensation. However, the White (1980) test rejects the joint hypothesis that the model is well-specified and homoscedastic, thus column A includes White-corrected p-values. In column A, the coefficients for the variables of interest, RETURNS and RET*VAR are both statistically and economically significant. Pay–performance sensitivity in this model is measured by b 1 + b 3VAR. A firm with minimum variance (i.e., VAR = 0) has pay–performance sensitivity of b 1, estimated as 0.021. Because both COMP and RETURNS are measured in millions, pay–performance sensitivity of 0.021 indicates that CEO compensation increases by $21 per $1,000 increase in firm value. This increase is much larger than the $3.25 per $1,000 estimated by Jensen and Murphy (1990); closely comparable to the $12.550 and $27.596 per $1,000 reported by AS (1999, Table 3, p. 80); and consistent with anecdotal evidence that CEO compensation in internet firms is heavily weighted towards performance-related pay. The statistically significant 0.02 coefficient on the interaction between performance and the CDF of variance of dollar returns (RET*VAR), indicates that pay–performance sensitivity declines dramatically as risk increases. For example, for a firm with median variance (VAR = 0.5), the pay–performance sensitivity drops to 0.021 0.020(0.5) = 0.011; that is, CEO pay increases by $11 per $1,000 increase in firm value. This represents a 47% decrease from the pay–performance sensitivity for the minimum variance firm illustrated above. Similarly, for a firm with the highest variance (VAR = 1), pay–performance sensitivity declines to 0.021 0.02 = 0.001, i.e., CEO pay increases by $1 for a $1,000 increase in value. This economically significant decline in pay–performance sensitivity from the lowest variance to the highest variance firm lends strong support to the prediction of principal–agent theory that pay–performance sensitivity is negatively related to risk. Although the CDF of dollar returns implicitly accounts for the tendency of larger firms to have larger variances (AS 1999), in column B we include additional controls for size in order to address the confounding effect size may have on pay–performance sensitivity. We add two size-related variables, SALES and RET*SALES. The inclusion of these size variables improves the fit of the model. The White (1980) test does not reject and adjusted R 2 increases from 57.6% to 65.6%.12 As in the base model, compensation is positively related to firm performance. The pay–performance sensitivity for the least variance, smallest firm is $9 per $1,000 increase in firm value. However, contrary to results using the base model, pay–performance sensitivity increases with risk after controlling for size — i.e., the coefficient on RET*VAR is significantly positive. The results from the sizeaugmented model, consistent with the predictions of Prendergast (2002, 2000), suggest that for Internet firms, the need for higher levels of incentive compensation due to 12
We do not mean to imply that the size augmented model is better because it has a higher adjusted R 2. We discuss this more in the robustness and conclusion sections.
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monitoring difficulty outweighs the trade-off of risk and incentives from the traditional agency theory model.13 Consistent with results in Aggarwal and Samwick (2002, 2003) and Jin (2002), we find a negative relation between pay–performance sensitivity and our size proxy, SALES.14 As discussed in Section 2.3, a negative coefficient on the interaction term RET*SALES has two possible explanations. SALES is either an additional risk proxy or it represents CEO wealth constraint effects. The risk proxy explanation does not seem plausible for our sample. We have explicitly controlled for risk by including variance of market returns in the model. As noted above, the coefficient estimate of RET*VAR is significantly positive, suggesting a positive relation between risk and pay–performance sensitivity consistent with Prendergast (2000, 2002). However, the sign on RET*SALES is negative, suggesting an inverse relation between risk and pay–performance sensitivity. It is not plausible that size and variance of returns have opposite signs if both are proxies for risk.15 Therefore, for our sample firms, we conclude that size is a proxy for CEO wealth constraints, and variance is the proxy for risk. Column C of Table 4 presents results from estimating Eq. (3). This model includes growth (GROWTH, RET*GROWTH), CEO ownership (OWN and RET*OWN), and leverage variables (LEV and RET*LEV) in addition to all variables used in Column B. The White (1980) test does not reject the joint hypothesis that the model is well specified and homoskedastic. As in the base and size augmented models, compensation is positively and significantly related to firm performance. However, the RET*VAR interaction variable, although positive, is no longer significant. Thus, pay–performance sensitivity is not associated with risk once we simultaneously control for size, growth, ownership, and leverage. As in the size augmented model, pay–performance sensitivity declines with SALES. Moreover, pay–performance sensitivity is negatively related to growth and CEO ownership. The negative relation between pay–performance sensitivity and CEO ownership is consistent with prior research that documents a substitution between higher CEO ownership and stock-based incentives (Core et al., 1999; Mehran, 1995; Engel et al., 2002; Smith and Watts, 1992; Lulseged and Christie, 2002; Nowlin and Christie, 2002).16 Both LEV and RET*LEV are insignificant suggesting that, in our sample of Internet firms, the pay–performance sensitivity is not related to leverage. This finding is consistent with evidence provided in Yermack (1995) and Garvey and Mawani (2003). 13 In Section 3.4, we conduct robustness tests using a sample from 2000–2002. We find that the results from the AS (1999) model that excludes size are more robust to changes in the economic environment. See Section 3.4. 14 To check the robustness of our findings to the way size is measured, we replace SALES with beginning market value. See Aggarwal and Samwick (2002) for a discussion of this. As in the base model, compensation is significantly positively related to firm performance. However, although negative, both the RET*VAR and RET*BEGMV variables are insignificantly different from zero, and the White (1980) test rejects the null hypothesis that the model is well specified and homoskedastic. 15 The focus of our analyses is on the sensitivity of pay to firm performance. Thus, when we refer to risk or size, we are referring to the interaction terms RET*VAR and RET*SALES. 16 Garvey and Swan (2002, p. 30) offer an alternative interpretation. They state that bstock illiquidity results in both high managerial ownership and limited use of stock options as an incentive device.Q
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3.4. Robustness of results In the above discussion, it appears that the effect of variance on pay–performance sensitivity is varying across models. Although each of these alternative results is consistent with some prior research and competing theoretical views, we explore the issue further by checking the robustness of the results to changes in the macro-economic environment over time. Our sample period, 1997 to 1999, was a period of time during which internet firms were experiencing rapid growth, high stock valuations, and price uncertainty. This raises the question as to what can be learned about fundamental finance relationships by examining internet firms during this period, and what the implications are for companies operating in the current environment. To test the robustness of our results to changes in the environment in which internet firms operate, we repeat our analyses using data on our sample firms for the period 2000 to 2002 (results not tabulated). We obtain data for 43 firm-years during this period. Consistent with our results for the base model, we find that compensation is positively related to returns, while pay–performance sensitivity declines with increases in the variance of dollar returns—i.e., there is a significantly negative coefficient on RET*VAR. When we add size to the model, compensation remains positively related to returns. Unlike our results for the earlier time period, however, the coefficient on RET*VAR remains significantly negative while size is insignificant. Our finding for the later period, that pay– performance sensitivity declines with increases in risk even after controlling for size suggests that the AS (1999) result is more robust to changes in the economic environment. Overall, our findings suggest that boards of directors of Internet firms take into account their company’s risk profile when designing CEO compensation contracts.
4. Summary and conclusions A major prediction of agency theory is that there is a trade-off between risk and incentive compensation. Using a sample of large firms from the ExecuComp database, Aggarwal and Samwick (1999) directly test and find results consistent with agency theory—pay–performance sensitivity is decreasing in risk. However, Prendergast (2002, 2000) offers a number of reasons why the sensitivity of pay to performance can be higher in risky environments, and notes that the use of options to compensate employees in hightech industries seems unlikely if the main determinant of pay–performance sensitivity is the trade-off of risk and incentives. We extend the empirical work of Aggarwal and Samwick (1999) and test the theoretical conjectures of Prendergast (2002, 2000) by examining the sensitivity of CEO compensation to market performance for a sample of high-risk companies—Internet firms. Because of their high volatility and increased growth opportunities, Internet firms provide a rich setting in which to examine the relation between CEO compensation and risk, the sensitivity of pay to performance, and other aspects of principal–agent theory. Typically, Internet firms are young, high-growth and highly volatile. Thus, there is a high degree of information asymmetry between the CEO and shareholders. This information asymmetry implies greater monitoring difficulties, leading to a higher
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demand for performance-based compensation in order to motivate the CEO to select valuemaximizing projects (Jensen and Meckling, 1976; Smith and Watts, 1992; Lulseged and Christie, 2002; Prendergast, 2002). This suggests a positive relation between incentive compensation and risk. On the other hand, tying compensation to firms’ market performance shifts risk away from well-diversified shareholders onto executives holding undiversified portfolios, and may result in inefficient risk sharing. In high risk Internet firms, tying compensation to market performance can be costly for a number of reasons, including the premium CEOs will demand for bearing the additional risk imposed on them by the compensation mix. This suggests that incentive-based compensation should decline with increases in risk (Core et al., 1999; Meulbroek, 2001; Jin, 2002; Lulseged and Christie, 2002; Nowlin and Christie, 2002). As in AS (1999), we find that pay–performance sensitivity declines with increases in risk in a base regression model that excludes other control variables. After controlling for size (net sales), we find that our model is better specified, and pay–performance sensitivity is positively related to risk, consistent with the predictions of Prendergast (2002, 2000). However, sensitivity tests on later periods show that the AS (1999) results are more robust to changes in the economic environment. Over all, we conclude that pay– performance sensitivity for internet firms decreases as variance increases consistent with the predictions of agency theory and the findings in AS (1999). The boards of directors of Internet firms seem to take into account their company’s risk profile when designing CEO compensation contracts. Due to data requirements, our sample includes only Internet firms that went public prior to 1998. Thus, the results of our study may not apply to private (or newly-public) Internet firms, because the nature of agency problems confronted by public firms differs from those encountered by private firms. In particular, most private Internet companies do not have to face the agency problems caused by separation of ownership and control, and thus their compensation packages may differ from those of public firms. Additionally, public firms have access to disciplining mechanisms of the market (such as the market for corporate control) that may reduce the demand for incentive compensation as a means of aligning the interests of shareholders and managers. Therefore, care should be taken in generalizing our findings to the overall population of Internet firms. Acknowledgements We thank an anonymous reviewer, Rajesh Aggarwal, Emeka Nwaeze, and workshop participants at Florida State University and the 2003 Frank Batten Young Scholars Forum in Accounting at the College of William and Mary, particularly Wanda Wallace and Audra Boone, for helpful comments. We thank Bob Russ and Joyce Vanderlaan Smith for research assistance. References Aggarwal, R., Samwick, A., 1999. The other side of the trade-off: the impact of risk on executive compensation. Journal of Political Economy 107, 65 – 105.
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Aggarwal, R., Samwick, A., 2002. The Other Side of the Trade-off: The Impact of Risk on Executive Compensation — a Reply (Working Paper). Tuck School of Business, Dartmouth University. Aggarwal, R., Samwick, A., 2003. Performance incentives within firms: the effect of managerial responsibility. Journal of Finance 58, 1613 – 1650. Agrawal, A., Knoeber, C., 1996. Firm performance and mechanisms to control agency problems between managers and shareholders. Journal of Financial and Quantitative Analysis 31, 377 – 397. Baker, G., Hall, B., 1998. CEO incentives and firm size. Journal of Labor Economics 22 (October), 767 – 798. Belsley, W.R., Kuh, F., Welsch, R.E., 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley and Sons, New York. Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637 – 654. Chadwick Investment Group, 2004. ISDEX Futures and Options Information. Available at http://www. chdwk.com/isdex.cfm. Christie, A.A., Joye, M.P., Watts, R.L., 2003. Decentralization of the firm: Theory and evidence. Journal of Corporate Finance 9 (January), 3 – 36. Core, J., Guay, W., 2002. The Other Side of the Trade-off: The Impact of Risk on Executive Compensation: A Revised Comment (Working Paper). University of Pennsylvania. Core, J., Holthausen, R., Larcker, D., 1999. Corporate governance, chief executive officer compensation and firm performance. Journal of Financial Economics 51, 371 – 406. Demsetz, H., Lehn, K., 1985. The structure of corporate ownership: causes and consequences. Journal of Political Economy 93, 1155 – 1177. Eaton, J., Rosen, H.S., 1983. Agency, delayed compensation and the structure of executive remuneration. Journal of Finance 38, 1489 – 1505. Engel, E., Gordon, E., Hayes, R., 2002. The roles of performance measures and monitoring in annual governance decisions in entrepreneurial firms. Journal of Accounting Research 40 (May), 485 – 518. Garvey, G., Mawani, A., 2003. Executive Stock Options and the Mediation of Stockholder–bondholder Conflicts (Working Paper). Claremont Graduate University. Garvey, G., Swan, P., 2002. Agency Problems are Ameliorated by Stock Market Liquidity: Monitoring, Information and the Use of Stock-based Compensation (Working Paper). University of New South Wales. Gaver, J., Gaver, K., 1993. Additional evidence on the association between the investment opportunity set and corporate financing, dividend, and compensation policies. Journal of Accounting and Economics 16, 125 – 160. Hand, J., 2000. Profits, Losses, and the Non-linear Pricing of Internet Stocks (Working Paper). University of North Carolina-Chapel Hill. Hand, J., 2001. The role of book income, web traffic, and supply and demand in the pricing of U.S. internet stocks. European Finance Review 5, 295 – 317. Jensen, M., 1986. The market for corporate control: agency costs of free cash flow, corporate finance and takeovers. American Economic Review 76, 323 – 329. Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3, 305 – 360. Jensen, M., Murphy, K., 1990. Performance pay and top-management incentives. Journal of Political Economy 98, 225 – 262. Jin, L., 2002. CEO compensation, diversification, and incentives. Journal of Financial Economics 66, 29 – 63. Larner, R., 1966. Ownership and control in the 200 largest nonfinancial corporations, 1929 and 1963. American Economic Review 56, 777 – 787. Lulseged, A., Christie, A., 2002. Efficient Contracting and Endogeneity in Corporate Governance and Compensation (Working Paper). Florida State University. Mehran, H., 1995. Executive compensation structure, ownership, and firm performance. Journal of Financial Economics 38, 163 – 184. Meulbroek, L., 2000. Does risk matter? Corporate Insider Transactions in Internet-based Firms (Working Paper). Claremont McKenna College. Meulbroek, L., 2001. The efficiency of equity-linked compensation: understanding the full cost of awarding executive stock options. Financial Management, 5 – 44 (Summer). Myers, S.C., 1977. Determinants of corporate borrowing. Journal of Financial Economics 5, 147 – 175.
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Nowlin, T., Christie, A., 2002. Efficient Contracting and Components of CEO Compensation in Initial Public Offerings (Working Paper). Virginia Commonwealth University. Prendergast, C., 2000. What trade-off risk and incentives? The American Economic Review 90 (2), 421 – 425. Prendergast, C., 2002. The tenuous trade-off between risk and incentives. Journal of Political Economy 110 (5), 1071 – 1102. Smith, C., Watts, R., 1992. The investment opportunity set and corporate financing, dividend, and compensation policies. Journal of Financial Economics 32, 263 – 292. Trueman, B., Wong, M.H., Zhang, X., 2000. The eyeballs have it: searching for the value in internet stocks. Journal of Accounting Research 38, 137 – 162. White, H., 1980. A heteroskedasticity-consistent covariance estimator and a direct test for heteroskedasticity. Econometrica 48, 817 – 838. Yermack, D., 1995. Do corporations award CEO stock options effectively? Journal of Financial Economics 39, 237 – 269.
Executive compensation and risk: The case of internet firms Carol Callaway Deea,*, Ayalew Lulsegeda, Tanya S. Nowlinb a
College of Business, Florida State University, Tallahassee, FL, 32306-1110, United States b University of Louisiana at Lafayette, United States
Received 30 November 2004; received in revised form 21 December 2004; accepted 26 December 2004 Available online 22 June 2005
Abstract A major prediction of agency theory is that there is a trade-off between risk and incentive compensation. Aggarwal and Samwick (1999) [Aggarwal, R., Samwick, A., 1999. The other side of the trade-off: the impact of risk on executive compensation. Journal of Political Economy, 107, 65– 105.] directly test and find results consistent with agency theory—pay-performance sensitivity is decreasing in risk. However, Prendergast (2002, 2000) [Prendergast, C. 2002. The tenuous trade-off between risk and incentives. Journal of Political Economy 110 (5), 1071–1102; Prendergast, C. 2000. What trade-off risk and incentives? The American Economic Review 90 (2), 421–425.] offers a number of reasons why the sensitivity of pay to performance can be higher in risky environments. We use data from a sample of Internet firms for 1997–1999 to provide empirical evidence on these competing arguments regarding the relation between risk and CEO compensation. Consistent with Aggarwal and Samwick (1999), our results show that pay–performance sensitivity declines with increases in variance in a base model. After controlling for size, we find that pay–performance sensitivity is positively related to risk, consistent with the theoretical predictions in Prendergast (2002, 2000). However, sensitivity tests in later periods show that the Aggarwal and Samwick (1999) results are more robust to changes in the economic environment. D 2005 Elsevier B.V. All rights reserved. JEL classification: M41; J33 Keywords: Executive compensation; Risk; Internet; New economy
* Corresponding author. Tel.: +1 850 644 7879; fax: +1 850 644 8234. E-mail address: [email protected] (C.C. Dee). 0929-1199/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jcorpfin.2004.12.002
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1. Introduction One of the main predictions of agency theory is that there is a trade-off between incentive compensation and risk. Agents must be compensated for bearing additional risk, resulting in higher wage costs. Thus, firms must trade off higher incentives against higher wage costs, which can lead to a reduction of incentive-based compensation as risk increases. Using a sample of large firms from the ExecuComp database, Aggarwal and Samwick (1999, hereafter AS 1999) directly test and find results consistent with the theory—pay–performance sensitivity is decreasing in risk. Jin (2002) also finds a negative relation between risk and pay–performance sensitivity in an augmented model controlling for variables that capture the value of CEO effort. Despite these recent findings, however, overall empirical evidence on the effect of risk on pay–performance sensitivity is mixed.1 Motivated by the mixed empirical findings, and the apparent inconsistency between the predictions of standard agency theory and anecdotal evidence, Prendergast (2002) proposes (but does not empirically test) an alternate theory. Prendergast (2002, 2000) offers a number of reasons why the sensitivity of pay to performance can be higher in risky environments. For example, he notes that in uncertain environments, it is more difficult for the principal to know on what projects the agent should be working. Thus, in riskier settings, it is less costly to reward the agent based on observed output (as opposed to monitoring his inputs), leading to a positive relation between pay–performance sensitivity and risk.2 In particular, Prendergast (2002, fn. 1) notes that the use of options to compensate employees in high-tech industries seems unlikely if the main determinant of pay–performance sensitivity is the trade-off of risk and incentives. In this paper we shed light on this debate by providing empirical evidence on the relation between pay–performance sensitivity and risk in internet firms—firms which are inherently high risk. Our study contributes to the existing literature in at least two ways. First, we document whether the principal–agent theory prediction of a negative association between pay–performance sensitivity and risk that AS (1999) find in a sample of large firms will also hold in inherently high-risk internet firms. Such evidence goes beyond what Aggarwal and Samwick showed, and is particularly interesting given the argument by Prendergast (2002, 2000) that one should not find a negative relation between incentives and risk in high-tech firms. Second, we provide evidence on the robustness of the findings in AS (1999) to the inclusion of additional control variables (such as size, growth, ownership, etc.) and to changes in the macro economic environment across time (1997– 1999 and 2000–2002). Internet firms offer a unique setting in which to examine the conflicting predictions on the relation between compensation and risk.3 Typically, Internet firms have more
1
Prendergast (2002, p. 1077) offers a summary of this research. Note that Prendergast (2002, 2000) does not suggest that a positive relation between risk and incentive compensation should exist, merely that credible reasons exist why that may be the case. 3 We define an internet firm as one bthat would not exist if it were not for the internet, and for which 51% or more of its revenue comes from or because of the internetQ (Hand, 2000, p. 2). 2
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intangible assets than assets in place, are high growth, and highly volatile.4 Consequently, there is a high degree of information asymmetry between the CEO and shareholders. High information asymmetry implies greater monitoring difficulty (based on inputs). Other things being equal, greater monitoring difficulty leads to a higher demand for performance-based compensation in order to motivate the CEO to select projects consistent with the maximization of shareholder wealth (Jensen and Meckling, 1976; Smith and Watts, 1992; Lulseged and Christie, 2002; Prendergast, 2002). This suggests a positive relation between compensation and risk. However, tying compensation to firms’ market performance shifts risk away from welldiversified shareholders onto executives holding undiversified portfolios, and may result in inefficient risk sharing. In high risk Internet firms, tying compensation to market performance can be very costly and even result in a decrease in shareholder value for several reasons. First, CEOs demand a premium for bearing the additional risk imposed on them by the compensation mix, and this cost increases with risk, particularly risk over which the CEO has no control (Core et al., 1999; Meulbroek, 2001; Jin, 2002; Lulseged and Christie, 2002; Nowlin and Christie, 2002). Second, CEOs will take actions to diversify their portfolio such as exercising stock options and selling the acquired shares. These acts of diversification by the CEO not only decrease the incentive alignment goal of the compensation, but also may reduce firm value if the market interprets the insider sale as a negative signal (Meulbroek, 2000). Third, Meulbroek (2001) finds that there is a dead weight loss (increasing with risk) associated with performance-based compensation. She notes that because the employee is undiversified, he or she values incentive stock options at an amount less than what the firm could obtain by selling the securities in the open market to a diversified investor. These reasons, consistent with the trade-off of risk and incentives in the traditional agency model, suggest that incentive-based compensation should decline with increases in risk. Given these competing arguments and conflicting predictions, we use data from a sample of Internet firms for 1997–1999 in order to provide empirical evidence on the issue. Our study extends the work of AS (1999) and offers evidence on the conjectures of Prendergast (2002, 2000) regarding the relation between risk and incentive-based compensation for CEOs in uncertain and rapidly changing technological environments. Our empirical models are based on AS (1999). Their model is based on principal–agent theory and lends itself to extensions that allow us to examine the relation between compensation and additional firm-specific characteristics. As in AS (1999), who examine a broad sample of large public companies, our results show that pay–performance sensitivity declines with increases in risk in a base model that excludes other control variables. After adding firm size (net sales) to the base model, results show that pay– performance sensitivity is positively related to risk, consistent with the claim of Prendergast (2002, 2000) that, in high-tech environments, the need for increased incentive
4 Our sample of internet firms differs in several respects from large, established firms typically studied in compensation research. For example, our firms are high growth, with high CEO percentage ownership and little debt. We discuss this more in Section 3.
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compensation due to monitoring difficulty outweighs the trade-off of risk and incentives from the traditional agency model. Our sample period is from 1997 to 1999, a period during which internet firms were experiencing rapid growth, elevated stock valuations, and high price uncertainty. This raises the question as to what can be learned about fundamental finance relationships by examining internet firms during this period, and what the implications are for today.5 To test the robustness of our results to changes in the environment in which internet firms operate, we repeat our analyses using data on our sample firms for the period 2000 to 2002. Consistent with our results for the base model, we find that compensation is positively related to returns, while pay–performance sensitivity declines with increases in the variance of dollar returns. Unlike our results for the earlier time period, however, when we add size to the model, compensation remains positively related to returns while pay– performance sensitivity still decreases with increases in risk. This finding for the later period, that pay–performance sensitivity declines with increases in risk even after controlling for size suggests that the AS (1999) result is more robust to changes in the economic environment. The next section presents the empirical model, and theory and prior research that motivates our research design. Section 3 presents empirical analyses, and the final section summarizes findings and conclusions.
2. Empirical model We estimate our models in three stages. First, we estimate a base model similar to AS (1999). Second, we add a control variable representing firm size (net sales) to the base model. Finally, as a robustness check, we augment the base model with size and other control variables representing firm characteristics that prior research finds to have an effect on total compensation and pay–performance sensitivity. 2.1. The base model In the base model, we regress compensation on dollar returns, the variance of dollar returns, the product of dollar returns and return variance, and year effect dummy variables. COMP ¼ b0 þ b1 RETURNS þ b2 VAR þ b3 RET*VAR þ b4 D1998 þ b5 D1999 þ e
ð1Þ
COMP is total flow compensation, i.e., the sum of cash compensation (salary and bonus) plus the value of options granted during the year. We collect compensation variables (including those needed to value options) from proxy statements available from either LexisNexis Academic Universe or the Securities and Exchange Commission’s
5
We thank the editor for this point.
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(SEC) Edgar website. We value stock options using the Black and Scholes (1973) optionpricing model modified for early exercise, with a risk-free rate of 6%.6 Consistent with AS (1999), we use annual dollar stock returns to shareholders, RETURNS, as our performance measure. To calculate RETURNS, we compute annual percentage returns from daily CRSP returns and multiply the percentage return by beginning market value (AS 1999). As in AS (1999), we use the variance of dollar returns as a proxy for risk. VAR is the cumulative distribution function (CDF) of the variance of dollar stock returns. To calculate VAR, we rank variances of the dollar stock returns for each firm, and then transform the ranks so that they lie uniformly between zero and one. RET*VAR is the product of RETURNS and VAR. D1998 and D1999 are year dummies for 1998 and 1999, included to control for possible year effects such as changes in the macroeconomic environment; e is the error term. 2.2. Model controlling for size A size proxy is added to the base model as an additional explanatory variable. Size can affect pay–performance sensitivity for several reasons. Size may be a proxy for risk (Jensen and Murphy, 1990; Aggarwal and Samwick, 2002, 2003), CEO wealth constraints (Demsetz and Lehn, 1985; Core and Guay, 2002), or the marginal product of CEO effort (Core et al., 1999; Smith and Watts, 1992; Gaver and Gaver, 1993). As in Jin (2002) and Aggarwal and Samwick (2003, 2002), we use sales as a proxy for size. COMP ¼ b0 þ b1 RETURNS þ b2 VAR þ b3 RET*VAR þ b4 D1998 þ b5 D1999 þ b6 SALES þ b7 RET*SALES þ e
ð2Þ
SALES is the CDF of net sales and all other variables are as defined before. The RET*SALES interaction term captures the effect of size on pay–performance sensitivity. 2.3. Model controlling for size and other firm characteristics Prior research documents that compensation is related to the underlying characteristics of the firm and the skill level of the CEO (Core et al., 1999; Smith and Watts, 1992; Gaver and Gaver, 1993). AS (1999) include a CEO fixed effect variable in their model. This variable is presumed to capture the effects of the underlying characteristics of the firm and the CEO’s skill level because in equilibrium firms are expected to hire CEOs with the skill level demanded by the job.
6 We use a term to expiration of four years because an examination of the proxy statements indicates that the average vesting period for stock option grants is 4 years. Firms in our sample have not paid out any dividends, so returns cum dividends are equal to returns ex dividends. For the firms with sufficient past return data, we use a maximum of 60 months of pre-1997 stock returns to compute the standard deviation of returns. Three firms have 60 months of historical returns data. For the remaining firms, the standard deviation calculation uses all available monthly returns (both pre-and post-compensation years) ending with December 1999. The actual number of months used in the calculation ranges from 25 to 60 per firm.
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We do not include CEO fixed effects in our model because of the reduction in degrees of freedom that would result. Instead, we include proxies for the underlying characteristics of firms that prior research finds to be related to compensation. Consistent with prior literature, we expect proxies for the underlying characteristics of the firm to capture the cross-sectional variation in the demand for CEO skill and the variation in CEO marginal productivity across firms. Similar to Smith and Watts (1992) and Gaver and Gaver (1993), we include proxies for growth and leverage in our extended model. We also include CEO ownership percentage as an explanatory variable. Our extended model is: COMP ¼ b0 þ b1 RETURNS þ b2 VAR þ b3 RET*VAR þ b4 D1998 þ b5 D1999 þ b6 SALES þ b7 RET*SALES þ b8 GROWTH þ b9 RET*GROWTH þ b10 OWN þ b11 RET*OWN þ b12 LEV þ b13 RET*LEV þ e
ð3Þ
GROWTH is the CDF of the market to book ratio: market value of equity plus book value of debt, divided by book value of assets (Smith and Watts, 1992). OWN is the CDF of the percentage ownership of the CEO. LEV is a proxy for leverage, and equals the CDF of the debt to equity ratio. All other variables are as previously defined. The variables GROWTH, OWN, and LEV also enter the regression multiplicatively with RETURNS (RET*GROWTH, RET*OWN, and RET*LEV). The interaction variables are included to control for the effects of size, growth, CEO ownership percentage, and leverage on the sensitivity of pay to performance. Our predictions for the effects of these variables on pay–performance sensitivity are based on their relation to incentive-based compensation. In general, because incentive-based compensation is more sensitive to firm performance than is fixed salary, a variable that is associated with increases (decreases) in incentive-based compensation will also be associated with increases (decreases) in pay–performance sensitivity. The problem of monitoring managers increases with both growth opportunities and firm size (Prendergast, 2002, 2000). Larger firms tend to be decentralized because of span-of control issues, thus exacerbating the monitoring problem (Eaton and Rosen, 1983; Christie et al., 2003). Additionally, the marginal productivity of CEO effort is higher for growth firms and large firms (Smith and Watts, 1992; Gaver and Gaver, 1993; Baker and Hall, 2004). For these reasons, growth firms and larger firms are more likely to use incentivebased compensation than are other firms (Smith and Watts, 1992; Gaver and Gaver, 1993). This would lead to a positive sign prediction on RET*SALES and RET*GROWTH. However, growth and size may be proxies for risk (Jensen and Murphy, 1990; Aggarwal and Samwick, 2002, 2003). Thus, if there is a tradeoff between risk and incentives as predicted by agency theory, pay–performance sensitivity may decline with increases in growth and size. Additionally, size may be negatively related to payperformance sensitivity due to CEO wealth constraint and risk aversion effects (Larner, 1966; Demsetz and Lehn, 1985; Core and Guay, 2002). These reasons would lead to negative sign predictions on RET*SALES and RET*GROWTH. Given these opposing predictions, the relation between pay–performance sensitivity and growth and size is an empirical issue. We make no sign prediction on SALES or
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GROWTH, because it is not clear whether size or growth are related to compensation after controlling for their effects on pay–performance sensitivity. Prior research finds that firms with high CEO ownership use incentive-based compensation less (Mehran, 1995; Agrawal and Knoeber, 1996; Core et al., 1999; Lulseged and Christie, 2002). Additionally, studies show that highly leveraged firms pay lower total compensation and use incentive-based compensation less in order to minimize managers’ incentives to increase the wealth of shareholders at the expense of debtholders (Smith and Watts, 1992; Gaver and Gaver, 1993; Agrawal and Knoeber, 1996; Lulseged and Christie, 2002). Therefore, we predict that the sensitivity of pay to market performance will decrease with both CEO ownership and leverage, because decreases in the amount of incentive-based compensation are associated with decreases in pay– performance sensitivity. That is, we predict a negative sign on both RET*OWN and RET*LEV. Because it is unclear whether CEO ownership or leverage is related to compensation after controlling for their effects on pay–performance sensitivity, we make no sign prediction on OWN or LEV.
3. Empirical analysis We present and discuss sample selection and descriptive statistics in Section 3.1, correlations among selected variables of interest in Section 3.2, regression results in Section 3.3, and robustness tests in Section 3.4. 3.1. Sample selection and descriptive statistics Our sample is selected from the population of 279 Internet firms listed on the bInternetStockListQ at May 6, 2000. This list is compiled by internet.com Corporation and has been used by prior researchers in selecting a sample of Internet firms (Hand, 2001, 2000; Trueman et al., 2000). The InternetStockList consists of all 50 firms included in the Internet stock index (ISDEX), along with many smaller Internet firms.7 In order to obtain sufficient post-IPO price data, financial data, and proxy statements, we limit our initial sample to firms that went public prior to January 1, 1998. There were 55 firms on the InternetStockList that met this criterion. We download proxy statements from either LexisNexis Academic Universe or the SEC’s Edgar database. Financial variables are from Standard & Poor’s Research Insight (Compustat) database. We omit firm-years for which (1) proxies are not available, (2) proxies do not contain enough compensation data needed for our tests, or (3) financial data is not available on Compustat. We exclude any years in which the CEO changed, or was not in office for the entire year. We delete one highly influential observation based upon our analysis of studentized residuals, DFFITS, and DFBETAS (Belsley et al., 1980).8 7
The ISDEX (a subset of the InternetStockList) is maintained by internet.com Corporation, and ISDEX futures are traded on the Kansas City Board of Trade. At February 12, 1999, the ISDEX represented 95% of the market capitalization of publicly-traded internet stocks (Chadwick Investment Group, 2004). 8 The 1999 observation for Broadvision was deleted.
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Table 1 Firms in final sample Name
Ticker
Name
Ticker
Amazon.com Inc America Online Inc At Home Corp AXENT Technologies Bluefly Broadvision Inc Cisco Systems CMGI Inc C/NET Concentric Network Crosswalk.Com CyberCash Cylink EarthLink Network eFax Egghead.com E*TRADE Group fine.com International Go2Net HomeCom Communications IDT Infonautics InterVU
AMZN AOL ATHM AXNT BFLY BVSN CSCO CMGI CNET CNCX AMEN CYCH CYLK ELNK EFAX EGGS EGRP FDOT GNET HCOM IDTC INFO ITVU
Lycos Inc NAVIDEC NetSpeak Network Associates Network Solutions Inc Open Market Peapod Preview Travel PSINet RealNetworks Inc RMI.NET Rogue Wave Software RSA Security Security First Technologies SportsLine USA Spyglass THINK New Ideas USWeb/CKS Visual Data V-ONE Voxware White Pine Software Yahoo!
LCOS NVDC NSPK NETA NSOL OMKT PPOD PTVL PSIX RNWK RMII RWAV RSAS SONE SPLN SPYG THNK USWB VDAT VONE VOXW WPNE YHOO
Table 1 lists the final sample, which consists of 46 separate firms with 104 firm-years of data.9 Note that, by design, our final sample differs from that of AS (1999). Their sample is selected from the ExecuComp database, which includes primarily large, well-established companies operating in a variety of industries. Our purpose, however, is to extend AS (1999) and empirically test the theoretical claims of Prendergast (2000, 2002) that predictions of the traditional agency model regarding risk and incentives may not hold for high-tech firms. Thus, we specifically focus on Internet firms in order to provide empirical evidence regarding the untested theory of Prendergast (2000, 2002). From each proxy statement, we gather compensation data (cash compensation and options granted) and CEO percentage ownership. We value the options granted using the Black–Scholes option pricing formula modified for early exercise, as described more fully in Section 2.1. Table 2 presents descriptive statistics. The sample firms have wide variability in size. Net sales range from $193,000 to $12.154 billion, with mean (median) values of $456.6 million ($49.3 million). The firms in the sample are, on average, larger than the IPO firms in Nowlin and Christie (2002), who report mean (median) sales of $53 million ($22
9
The tenor of our conclusions does not change when we conduct our analyses using only firms that have complete data for all three years.
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Table 2 Descriptive statistics (n = 104) Minimum
Mean
Median
Maximum
Std. dev.
Firm characteristics Sales* 0.193 456.614 49.300 12,154.000 1641.520 Net Income* 1457.640 15.901 8.801 2096.000 334.753 Total assets* 3.099 779.909 81.082 14,725.000 2079.890 Beginning market value* 5.230 2883.129 167.927 97,512.470 11,707.036 Return on assets 2.399 0.282 0.207 0.819 0.419 Annual market return 0.602 1.505 0.508 16.267 2.650 Annual market return (dollars)* 1094.470 3716.75 46.034 92,292.98 13,693.090 Standard deviation of market return 0.101 0.281 0.280 0.489 0.064 CDF of variance of dollar returns 0.006 0.500 0.500 0.986 0.289 Dollar returns*CDF of variance of dollar returns 0.899 3.477 0.016 91.036 13.235 Leverage (debt to equity) 0 0.075 0.004 0.829 0.167 Growth (market to book) 0.885 6.723 4.153 78.565 9.255 Compensation variables Cash compensation* Total compensation* % option value to total compensation
0.173 0.070 0
0.311 10.670 50.555
0.232 0.745 65.662
1.664 151.294 99.839
0.273 27.012 42.255
Others CEO ownership %
0
10.996
3.780
70.800
13.942
*Millions of dollars.
million). Market values are right-skewed, with a mean value of $2.883 billion and median of $167.9 million. Mean (median) net income is $15.9 million ($ 8.8 million), and mean (median) return on assets is 28.2% ( 20.7%). However, the mean (median) annual return is 150.5% (50.8%). The mean (median) standard deviation of percentage returns is 28.1% (28.0%), indicating the high volatility that characterizes such firms.10 Further, untabulated results show that 76% of the firms have net losses, yet only 31% have negative returns. Negative earnings are typical of Internet firms during our sample period. Hand (2001, 2000) reports negative earnings for 87% of the firms in his sample, while Engel et al. (2002) report negative earnings for 85% of their sample Internet firms during their post-IPO period. The mean (median) ratio of market to book in our sample is 6.7 (4.2). These growth numbers are much higher than those in other studies using larger, more established firms. For example, in a 1993 sample of S&P 500 firms, Lulseged and Christie (2002) report mean and median growth of 1.6 and 1.3, respectively. Not surprisingly, our sample firms have little debt, as evidenced by the low mean (median) debt to equity ratio of 7.5% (0.4%). This is consistent with predictions in agency theory and evidence from prior research that growth firms and firms with high levels of intangible assets have lower leverage. Internet firms are high growth firms; and high 10 For a sample of IPO firms from 1996–1997, Nowlin and Christie (2002) report mean and median standard deviation of returns of 23%. Lulseged and Christie (2002) report a mean (median) standard deviation of returns of 7.5% (7.1%) in a sample of S&P 500 firms for 1993.
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Table 3 Correlation table (N = 104) Variable
MV
MV SALES GROWTH VAR OWN LEV
1 0.819 0.325 0.827 0.330 0.068
SALES (0.001) (0.001) (0.001) (0.001) (0.495)
1 0.235 0.737 0.246 0.174
(0.016) (0.001) (0.012) (0.077)
GROWTH
VAR
OWN
LEV
1 0.465 (0.001) 0.107 (0.280) 0.001 (0.991)
1 0.189 (0.054) 0.108 (0.274)
1 0.285 (0.003)
1
Pearson Correlation coefficients ( p-values in parentheses). MV equals the CDF of the market value of equity at the beginning of the year. SALES is the CDF of net sales. GROWTH is the CDF of the market-to-book ratio (market value of equity plus book value of debt, divided by book value of total assets.) VAR is the CDF of dollar return variance. OWN is the CDF of CEO ownership percentage. LEV equals the CDF of the debt-to-equity ratio.
growth firms tend to have lower debt to equity ratios (Myers, 1977; Jensen, 1986; Smith and Watts, 1992; Gaver and Gaver, 1993). Additionally, most of these firms have had a recent cash infusion resulting from a public offering. In our sample, CEO ownership percentages are high, with a mean (median) of 11% (3.8%). This is comparable to the sample of IPO firms in Nowlin and Christie (2002), who report mean (median) CEO ownership percentages of 17.6% (10.2%), and to the post IPO sample of Internet firms in Engel et al. (2002), who report mean (median) CEO ownership percentages of 10.4% (5.6%). However, as noted by Nowlin and Christie (2002) it is much higher than the typical CEO ownership percentage reported in studies of large, nonInternet firms. For a sample of large firms, Core et al. (1999) report mean (median) CEO ownership percentages of 1.5% (0.09%). The high percentage ownership for our sample firms implies that there may be less need for stock-based compensation for these firms. For the CEOs in our sample, cash compensation (salary plus bonus) is relatively modest compared to total compensation. Mean (median) cash compensation is $311,000 ($232,000). Total compensation, which equals cash compensation plus the value of options granted, is right-skewed, with mean (median) values of $10.7 million ($745,000). Additionally, the median percentage option value to total compensation is 66%. These statistics show that these firms rely heavily on options to compensate their CEOs. 3.2. Correlation analysis Table 3 presents Pearson correlations among selected variables of interest. We find a significantly positive (0.827) correlation between variance of dollar returns (VAR) and market value (MV).11 The correlation between variance of dollar returns (VAR) and SALES, although slightly lower, is significantly positive (0.737). Market value and growth 11 Both Core and Guay (2002) and AS (1999) calculate variance of returns using 60 monthly returns prior to the beginning of the year. We measure variance of returns using 60 monthly returns prior to the beginning of 1997, whenever we have data. When a firm has no or some past history (less than 60 months) prior to the beginning of 1997, we calculate variance of returns using as many monthly returns as possible (at least 21 and at most 60 monthly returns) before and after the beginning of 1997.
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are positively correlated (0.325). This correlation is partly driven by the way we measure the two variables. Growth is measured as the ratio of market value of equity plus the book value of debt to book value of assets. Thus, the numerator in the growth measure includes the market value of equity and hence it is not surprising that we find this positive correlation. Consistent with the prediction of agency theory that firms with high leverage have higher managerial ownership (Jensen and Meckling, 1976), we find a positive correlation of 0.285 between leverage and CEO ownership (OWN). The negative correlation between CEO ownership and market value ( 0.330) is consistent with results in Demsetz and Lehn (1985) and Core and Guay (2002). 3.3. Regression results Table 4 presents regression results. There are two main differences between our models and those of AS (1999). First, as discussed in footnote 6, we calculate the variance of dollar returns somewhat differently than they do because of data limitations. Second, we use ordinary least squares (OLS) regressions rather than median regressions used by AS (1999), although AS (1999) present OLS results as well. AS (1999) note that the precision of parameter estimates is higher in median regressions, because the median is a more
Table 4 Regression results (COMP= b 0 + b 1RETURNS + b 2VAR + b 3RET * VAR + b 4D1998 + b 5D1999 + b 6SALES + b 7 RET * SALES + b 8 GROWTH + b 9 RET * GROWTH + b 10 OWN + b 11 RET * OWN + b 12 LEV + b 13 RET * LEV + e) A* Intercept RETURNS VAR RET*VAR SALES RET*SALES GROWTH RET*GROWTH OWN RET*OWN LEV RET*LEV Adj. R2 Prob. F
2.380 0.021 12.144 0.020
0.576 0.001
B (0.618) (0.001) (0.083) (0.001)
C
4.052 0.009 1.285 0.016 14.037 0.025
0.656 0.001
(0.352) (0.051) (0.885) (0.056) (0.107) (0.001)
4.686 0.028 5.273 0.013 13.284 0.034 1.534 0.007 9.748 0.003 2.930 0.001 0.712 0.001
(0.432) (0.001) (0.568) (0.204) (0.122) (0.001) (0.804) (0.006) (0.098) (0.011) (0.606) (0.346)
p-values are in parentheses. COMP equals cash salary and bonus, plus value of options granted during the year (in millions). RETURNS equals annual percentage return times beginning market value in millions. RET*VAR is an interaction term equal to RETURNS times VAR. D1998 is an indicator variable equal to one if the year is 1998, zero otherwise. D1999 is an indicator variable equal to one if the year is 1999, zero otherwise. SALES is the CDF of net sales. RET*SALES is equal to RETURNS times SALES. GROWTH is the CDF of the market-to-book ratio (market value of equity plus book value of debt, divided by book value of total assets.) RET*GROWTH equals RETURNS times GROWTH. OWN is the CDF of CEO ownership percentage. RET*OWN equals RETURNS times OWN. LEV equals the CDF of the debt-to-equity ratio. RET*LEV equals RETURNS times LEV. *p-values in Column A are calculated using asymptotic standard errors (White 1980). Parameter estimates for both D1998 and D1999 are insignificant in all models, and omitted for brevity.
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robust measure of the center of the data than the mean. Using OLS will thus work against our finding significant results. Column A presents our base model. The results are very similar to those reported by AS (1999; Table 6, p. 88). Compensation is positively associated with returns, and, consistent with the predictions of principal–agent theory, pay–performance sensitivity declines with increases in the variance of dollar returns. Both year dummies (not reported in the table) are insignificant. The model explains about 58% of the variation in total compensation. However, the White (1980) test rejects the joint hypothesis that the model is well-specified and homoscedastic, thus column A includes White-corrected p-values. In column A, the coefficients for the variables of interest, RETURNS and RET*VAR are both statistically and economically significant. Pay–performance sensitivity in this model is measured by b 1 + b 3VAR. A firm with minimum variance (i.e., VAR = 0) has pay–performance sensitivity of b 1, estimated as 0.021. Because both COMP and RETURNS are measured in millions, pay–performance sensitivity of 0.021 indicates that CEO compensation increases by $21 per $1,000 increase in firm value. This increase is much larger than the $3.25 per $1,000 estimated by Jensen and Murphy (1990); closely comparable to the $12.550 and $27.596 per $1,000 reported by AS (1999, Table 3, p. 80); and consistent with anecdotal evidence that CEO compensation in internet firms is heavily weighted towards performance-related pay. The statistically significant 0.02 coefficient on the interaction between performance and the CDF of variance of dollar returns (RET*VAR), indicates that pay–performance sensitivity declines dramatically as risk increases. For example, for a firm with median variance (VAR = 0.5), the pay–performance sensitivity drops to 0.021 0.020(0.5) = 0.011; that is, CEO pay increases by $11 per $1,000 increase in firm value. This represents a 47% decrease from the pay–performance sensitivity for the minimum variance firm illustrated above. Similarly, for a firm with the highest variance (VAR = 1), pay–performance sensitivity declines to 0.021 0.02 = 0.001, i.e., CEO pay increases by $1 for a $1,000 increase in value. This economically significant decline in pay–performance sensitivity from the lowest variance to the highest variance firm lends strong support to the prediction of principal–agent theory that pay–performance sensitivity is negatively related to risk. Although the CDF of dollar returns implicitly accounts for the tendency of larger firms to have larger variances (AS 1999), in column B we include additional controls for size in order to address the confounding effect size may have on pay–performance sensitivity. We add two size-related variables, SALES and RET*SALES. The inclusion of these size variables improves the fit of the model. The White (1980) test does not reject and adjusted R 2 increases from 57.6% to 65.6%.12 As in the base model, compensation is positively related to firm performance. The pay–performance sensitivity for the least variance, smallest firm is $9 per $1,000 increase in firm value. However, contrary to results using the base model, pay–performance sensitivity increases with risk after controlling for size — i.e., the coefficient on RET*VAR is significantly positive. The results from the sizeaugmented model, consistent with the predictions of Prendergast (2002, 2000), suggest that for Internet firms, the need for higher levels of incentive compensation due to 12
We do not mean to imply that the size augmented model is better because it has a higher adjusted R 2. We discuss this more in the robustness and conclusion sections.
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monitoring difficulty outweighs the trade-off of risk and incentives from the traditional agency theory model.13 Consistent with results in Aggarwal and Samwick (2002, 2003) and Jin (2002), we find a negative relation between pay–performance sensitivity and our size proxy, SALES.14 As discussed in Section 2.3, a negative coefficient on the interaction term RET*SALES has two possible explanations. SALES is either an additional risk proxy or it represents CEO wealth constraint effects. The risk proxy explanation does not seem plausible for our sample. We have explicitly controlled for risk by including variance of market returns in the model. As noted above, the coefficient estimate of RET*VAR is significantly positive, suggesting a positive relation between risk and pay–performance sensitivity consistent with Prendergast (2000, 2002). However, the sign on RET*SALES is negative, suggesting an inverse relation between risk and pay–performance sensitivity. It is not plausible that size and variance of returns have opposite signs if both are proxies for risk.15 Therefore, for our sample firms, we conclude that size is a proxy for CEO wealth constraints, and variance is the proxy for risk. Column C of Table 4 presents results from estimating Eq. (3). This model includes growth (GROWTH, RET*GROWTH), CEO ownership (OWN and RET*OWN), and leverage variables (LEV and RET*LEV) in addition to all variables used in Column B. The White (1980) test does not reject the joint hypothesis that the model is well specified and homoskedastic. As in the base and size augmented models, compensation is positively and significantly related to firm performance. However, the RET*VAR interaction variable, although positive, is no longer significant. Thus, pay–performance sensitivity is not associated with risk once we simultaneously control for size, growth, ownership, and leverage. As in the size augmented model, pay–performance sensitivity declines with SALES. Moreover, pay–performance sensitivity is negatively related to growth and CEO ownership. The negative relation between pay–performance sensitivity and CEO ownership is consistent with prior research that documents a substitution between higher CEO ownership and stock-based incentives (Core et al., 1999; Mehran, 1995; Engel et al., 2002; Smith and Watts, 1992; Lulseged and Christie, 2002; Nowlin and Christie, 2002).16 Both LEV and RET*LEV are insignificant suggesting that, in our sample of Internet firms, the pay–performance sensitivity is not related to leverage. This finding is consistent with evidence provided in Yermack (1995) and Garvey and Mawani (2003). 13 In Section 3.4, we conduct robustness tests using a sample from 2000–2002. We find that the results from the AS (1999) model that excludes size are more robust to changes in the economic environment. See Section 3.4. 14 To check the robustness of our findings to the way size is measured, we replace SALES with beginning market value. See Aggarwal and Samwick (2002) for a discussion of this. As in the base model, compensation is significantly positively related to firm performance. However, although negative, both the RET*VAR and RET*BEGMV variables are insignificantly different from zero, and the White (1980) test rejects the null hypothesis that the model is well specified and homoskedastic. 15 The focus of our analyses is on the sensitivity of pay to firm performance. Thus, when we refer to risk or size, we are referring to the interaction terms RET*VAR and RET*SALES. 16 Garvey and Swan (2002, p. 30) offer an alternative interpretation. They state that bstock illiquidity results in both high managerial ownership and limited use of stock options as an incentive device.Q
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3.4. Robustness of results In the above discussion, it appears that the effect of variance on pay–performance sensitivity is varying across models. Although each of these alternative results is consistent with some prior research and competing theoretical views, we explore the issue further by checking the robustness of the results to changes in the macro-economic environment over time. Our sample period, 1997 to 1999, was a period of time during which internet firms were experiencing rapid growth, high stock valuations, and price uncertainty. This raises the question as to what can be learned about fundamental finance relationships by examining internet firms during this period, and what the implications are for companies operating in the current environment. To test the robustness of our results to changes in the environment in which internet firms operate, we repeat our analyses using data on our sample firms for the period 2000 to 2002 (results not tabulated). We obtain data for 43 firm-years during this period. Consistent with our results for the base model, we find that compensation is positively related to returns, while pay–performance sensitivity declines with increases in the variance of dollar returns—i.e., there is a significantly negative coefficient on RET*VAR. When we add size to the model, compensation remains positively related to returns. Unlike our results for the earlier time period, however, the coefficient on RET*VAR remains significantly negative while size is insignificant. Our finding for the later period, that pay– performance sensitivity declines with increases in risk even after controlling for size suggests that the AS (1999) result is more robust to changes in the economic environment. Overall, our findings suggest that boards of directors of Internet firms take into account their company’s risk profile when designing CEO compensation contracts.
4. Summary and conclusions A major prediction of agency theory is that there is a trade-off between risk and incentive compensation. Using a sample of large firms from the ExecuComp database, Aggarwal and Samwick (1999) directly test and find results consistent with agency theory—pay–performance sensitivity is decreasing in risk. However, Prendergast (2002, 2000) offers a number of reasons why the sensitivity of pay to performance can be higher in risky environments, and notes that the use of options to compensate employees in hightech industries seems unlikely if the main determinant of pay–performance sensitivity is the trade-off of risk and incentives. We extend the empirical work of Aggarwal and Samwick (1999) and test the theoretical conjectures of Prendergast (2002, 2000) by examining the sensitivity of CEO compensation to market performance for a sample of high-risk companies—Internet firms. Because of their high volatility and increased growth opportunities, Internet firms provide a rich setting in which to examine the relation between CEO compensation and risk, the sensitivity of pay to performance, and other aspects of principal–agent theory. Typically, Internet firms are young, high-growth and highly volatile. Thus, there is a high degree of information asymmetry between the CEO and shareholders. This information asymmetry implies greater monitoring difficulties, leading to a higher
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demand for performance-based compensation in order to motivate the CEO to select valuemaximizing projects (Jensen and Meckling, 1976; Smith and Watts, 1992; Lulseged and Christie, 2002; Prendergast, 2002). This suggests a positive relation between incentive compensation and risk. On the other hand, tying compensation to firms’ market performance shifts risk away from well-diversified shareholders onto executives holding undiversified portfolios, and may result in inefficient risk sharing. In high risk Internet firms, tying compensation to market performance can be costly for a number of reasons, including the premium CEOs will demand for bearing the additional risk imposed on them by the compensation mix. This suggests that incentive-based compensation should decline with increases in risk (Core et al., 1999; Meulbroek, 2001; Jin, 2002; Lulseged and Christie, 2002; Nowlin and Christie, 2002). As in AS (1999), we find that pay–performance sensitivity declines with increases in risk in a base regression model that excludes other control variables. After controlling for size (net sales), we find that our model is better specified, and pay–performance sensitivity is positively related to risk, consistent with the predictions of Prendergast (2002, 2000). However, sensitivity tests on later periods show that the AS (1999) results are more robust to changes in the economic environment. Over all, we conclude that pay– performance sensitivity for internet firms decreases as variance increases consistent with the predictions of agency theory and the findings in AS (1999). The boards of directors of Internet firms seem to take into account their company’s risk profile when designing CEO compensation contracts. Due to data requirements, our sample includes only Internet firms that went public prior to 1998. Thus, the results of our study may not apply to private (or newly-public) Internet firms, because the nature of agency problems confronted by public firms differs from those encountered by private firms. In particular, most private Internet companies do not have to face the agency problems caused by separation of ownership and control, and thus their compensation packages may differ from those of public firms. Additionally, public firms have access to disciplining mechanisms of the market (such as the market for corporate control) that may reduce the demand for incentive compensation as a means of aligning the interests of shareholders and managers. Therefore, care should be taken in generalizing our findings to the overall population of Internet firms. Acknowledgements We thank an anonymous reviewer, Rajesh Aggarwal, Emeka Nwaeze, and workshop participants at Florida State University and the 2003 Frank Batten Young Scholars Forum in Accounting at the College of William and Mary, particularly Wanda Wallace and Audra Boone, for helpful comments. We thank Bob Russ and Joyce Vanderlaan Smith for research assistance. References Aggarwal, R., Samwick, A., 1999. The other side of the trade-off: the impact of risk on executive compensation. Journal of Political Economy 107, 65 – 105.
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