We have examined the sensitivity of employee stock option grants to firm performance for top executives and separately for other employees using a large sample of firms from Execucomp during 1996-99. Our regression results of stock option grant values on firm stock price suggest substantial pay-for-performance sensitivities for top executives and for employees below the senior ranks. However, counter to agency models that would predict lower
sensitivities for employees whose actions have less effect on stock price, the pay-for-performance sensitivities for employees outside of top management is on average almost twice the size of that for top executives.
22 Nearly all stock options are granted at the money. Therefore, if all options were granted at the end of the year,
there would be a built in positive correlation between price at the end of the year and option grants during the year because the value of the option grant would contain the stock price at the end of the year.
We explore a number of hypotheses consistent with the agency framework to explain this difference. To some extent, the lower sensitivity of option grants appears to reflect that top executives have higher ownership stakes than middle management, but elasticities for top executives that have low ownership are still lower than those for middle management. In addition, firms offset price declines by granting more options to top executives to mitigate adverse incentives to take on greater risks or reduce effort when options are underwater, but do not do so for middle management whose actions have less effect. These results suggest that the asymmetric effect of price changes on incentives arising from options can explain some of the difference in pay-for-performance elasticities between top executives and middle management, but not the entire difference. Performance elasticities are equally disparate at new economy technology firms, where it might be assumed that middle management has relatively greater effect on firm value. We also find that stock option grants to both top executives and middle management are related to industry performance, reflecting greater demand for executives with skills to manage large organizations when industry stock prices rise. Moreover, this industry effect increases substantially with the size of the firm, consistent with Himmelberg and
Hubbard’s hypothesis that the supply of top executives with the ability to run large organizations is relatively inelastic.
We find that none of these factors can adequately explain the relatively higher pay-for- performance sensitivity for middle management. Thus, our results may be consistent with greater demand from middle management employees for options in periods of rising prices, which can be viewed as paying for the chance to become very wealthy. Such behavior has been
documented in other studies of investor behavior: Benartzi (2001) shows that purchases of
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company stock in 401(k) plans increases with past returns, consistent with excessive extrapolation of returns, and Lambert and Larcker (2001) survey results show employees
overestimate the value of their employee stock options, relative to Black-Scholes values, and that the overestimate is higher when previous returns have been higher.
Our results also have implications for the aggregate use of stock options. Our estimates suggest that much of the explosive growth in the use of employee stock options has been due to rising stock prices, at the firm and industry level. Using the coefficient on stock price from the baseline specification, we estimate that the change in stock prices can explain about two-thirds of the increase in the value of stock options granted by S&P 1500 firms during 1996-99. These results imply that when stock prices fall, the willingness of middle management to accept options in lieu of cash compensation will diminish. Because the value of employee stock option grants is not recorded as a compensation expense under current accounting standards, as are cash wages, the reluctance of employees to accept options could result in higher expenses and lower reported profits when the stock market turns down. An interesting topic for future research would be to compare beliefs of expected stock returns held by mid-level employees with those held by upper management, and to examine how firms adjust compensation packages of cash and options.
The results also shed some light on critics’ claims that the enormous gains realized by top executives during the raging bull market have been due to “luck” and, hence, in some sense undeserved. Our results provide some evidence that this may not be the case. For example, after controlling for industry performance, executives in firms that have performed better have
received more compensation via options. Moreover, large industry performance gains could reflect tighter labor markets for a limited supply of top executives with the skills to manage large
organizations. Our finding of a positive coefficient on industry performance that increases with assets is consistent with firms granting more employee stock options in response to heightened labor market pressures.
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References
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Benartzi, S, 2001, Excessive extrapolation and the allocation of 401(k) accounts to company stock, Journal of Finance 56: 1747-64.
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Table 1: Employee Stock Option Grants by S&P 1500 Firms
Number of stock option grants (percent of shares outstanding)
1996 1997 1998 1999
All S&P 1500 firms 1.41 1.62 1.66 1.83
By firm size:
S&P 500 1.30 1.47 1.49 1.68
S&P small-cap & mid-cap 1.86 2.22 2.49 2.70
By industry:
Technology 2.28 2.55 2.50 2.52
Non-Technology 1.21 1.38 1.40 1.50
By employee type:
Top executives (total for top five) .23 .26 .25 .25
CEO only .10 .12 .11 .10
Middle management 1.18 1.37 1.41 1.58
Ratio of top executive option grants to firm-wide grants
.17 .17 .15 .14
Fraction of firms that grant options .93 .94 .95 .96
Value of stock option grants per employee ($)
1996 1997 1998 1999
All S&P 1500 firms 1058 1475 1878 2508
By firm size:
S&P 500 993 1440 1861 2681
S&P small-cap & mid-cap 1199 1547 1911 2108
By industry:
Technology 2290 3036 4063 5811
Non-Technology 792 1116 1366 1667
By employee type:
Top executives (per executive) 427,600 604,800 772,200 1,167,200 CEO only 1,109,900 1,742,800 2,006,600 2,468,000
Middle management 879 1232 1589 2159
Top-executives cash salary and bonus 557,400 578,900 615,700 725,700 CEO cash salary and bonus 1,028,500 1,070,200 1,123,700 1,300,600 Computations based on Execucomp database and various 10-Ks.
The number of grants as a percent of shares outstanding is weighted by the average of beginning and end of year market value across firms to compute the aggregate numbers. Grant values per employee are weighted by the number of employees across firms to compute the aggregate numbers.
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Table 2:
Regression of Compensation per Employee on Firm Stock Price 1996-99, S&P 1500 Firms
(standard errors in parentheses)
Stock Option Grant Value: All Employees Stock Option Grant Value: Top Executives Grant Value: Middle Management
Cash Salary & Bonus 1996-99:
Top Executives
Cash Salary & Bonus 1992-95: Top Executives Log (Stock Price) .45 (.04) .29 (.05) .53 (.05) -.00 (.01) .07 (.01) 1996 dummy -.36 (.04) -.66 (.04) -.32 (.04) -.25 (.01) 1997 dummy -.17 (.04) -.41 (.04) -.15 (.04) -.17 (.01) 1998 dummy -.08 (.03) -.21 (.04) -.08 (.04) -.12 (.01) 1999 omitted 1992 dummy -.15 (.01) 1993 dummy -.11 (.01) 1994 dummy -.05 (.01) 1995 omitted within-firm R2 .089 .126 .091 .154 .116 # of observations 5677 5128 5747 6365 5987
Regressions are estimated over panel data from 1996-99 (1992-1995 for the last column) obtained from the Execucomp database and various 10-Ks. The basic regression specification is:
Table 3:
Regression of Stock Option Grant Value per Employee on Firm Stock Price 1996-99, S&P 1500 Firms
(standard errors in parentheses)
All Employees Top Executives
Middle Management Regression coefficients on price when split sample by ownership share:
Bottom quartile of ownership
(each top firm executive owns < .15% of firm on average over 1997-98) .53 (.09) .41 (.10) .63 (.10) Middle two quartiles of ownership
(each top firm executive owns between .15-1.23% of firm on average over 1997-98)
.46 (.06) .34 (.07) .50 (.06) Top quartile of ownership
(each top firm executive owns > 1.23% of firm on average over 1997-98) .34 (.08) .06 (.09) .48 (.08) p-value of test for equality between top quartile and
bottom quartile
.116 .007 .218 Regression coefficients on price when split sample by stock returns:
Bottom quartile of returns (average stock return < 6.9% per year)
.35 (.09) .04 (.10) .42 (.10) Middle two quartiles of returns
(average stock return between 6.9-42.7%)
.47 (.07) .30 (.08) .54 (.07) Top quartile of returns
(average stock return > 42.7% per year)
.50 (.07) .46 (.08) .59 (.07) p-value of test for equality between top quartile and
bottom quartile
.200 .001 .160 Regressions estimated over all employees have 5677 observations, middle management regressions have 5647 observations, and top executive regressions have 5128 observations (except for the cash-based salary regression, which has 5833 observations). Regressions are estimated over panel data from 1996-99 obtained from the Execucomp database and various 10-Ks. The basic regression specification is:
Log (grant value / employeei,t) = β*Log (price beginning of yeari,t) + firmi + yeart + εi,t
The sample is first divided into three groups by average ownership share over 1997-98 (top panel). We estimate a second regression where the sample is instead divided into three groups by average stock returns over 1996-99 (bottom panel). The three groups are the bottom quartile, middle two quartiles, and top quartile of either average ownership share or average stock returns. The coefficient on log price is then allowed to vary across the three groups.
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Table 4: Industry Prices
(all industry prices normalized to 100 at end of 1995)
Value end of 1997 Value end of 1999
Mining 100.8 97.5
Oil & Gas 178.5 143.0
Construction 119.8 116.2 Food 162.3 140.7 Basic Materials 141.5 162.4 Biotech / Medical 166.4 225.4 Manufacturing 147.4 167.0 Transportation 147.9 145.5 Telecom 146.9 275.7 Utilities 135.6 123.1
Retail / Wholesale Trade 159.4 219.9
Financial 195.0 218.2
Technology 174.1 420.5
Other 131.7 157.7
Returns (weighted by market value) are calculated for the fourteen industry groups using the entire sample of Execucomp firms (S&P 1500). The returns are then used to construct a time series of industry prices.
Table 5: Regression of Stock Option Grant Values upon both Own and Industry Prices
(standard errors in parentheses)
All Employees Top Executives
Middle Management Regression coefficients for all S&P 1500 firms
Firm Price .43 (.04) .27 (.05) .52 (.05) Industry Price .26 (.13) .43 (.14) .26 (.13) Regression coefficients for all S&P 1500 firms
(allow coefficient on Industry Price to vary with size of firms)
Firm Price .41 (.05) .24 (.05) .48 (.05)
Industry Price Variables Bottom quartile of size
(Average book value of firm’s assets < $357 million over 1997-98) -.02 (.15) .15 (.17) -.07 (.16) Middle two quartiles of size
(Average book value of firm’s assets between $357- 3700 million over 1997-98) .29 (.14) .47 (.15) .29 (.14) Top quartile of size
(Average book value of firm’s assets > $3700 million over 1997-98) .48 (.15) .59 (.17) .55 (.16) p-value of test for equality between top quartile and
bottom quartile
.001 .007 .000 Regressions estimated over all employees have 5677 observations, middle management regressions have 5647 observations, and top executive regressions have 5128 observations. Regressions are estimated over panel data from 1996-99 obtained from the Execucomp database and various 10-Ks. The basic regression specification is: Log (grant value / employeei,t) = β*Log (pricei,t) + α*Log (industry pricei,t) + firmi + yeart + εi,t
Prices are measured at the beginning of the year. Fourteen industry indices (prices) are constructed over the sample period. The industry groupings are mining, oil and gas, construction, food, basic materials, biotech/medical, manufacturing, transportation, telecom, utilities, retail/wholesale trade, financial, technology, and other. In the bottom panel, the industry price variable is divided into three groups by firm size (book value of assets averaged over 1997-98). The coefficient on log industry price is then allowed to vary across the three firm size groups.
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Table 6:
Regression of Stock Option Grant Value with Interactions 1996-99
(standard errors in parentheses)
All Employees Top Executives
Middle Management Own Price Coefficients
Coefficient .44 (.08) .31 (.09) .47 (.09) Interaction with low executive share ownership .02
(.11)
.05 (.12)
.06 (.11) Interaction with high executive share ownership -.10
(.10)
-.32 (.11)
-.00 (.10) Interaction with low stock return -.12
(.12)
-.27 (.13)
-.11 (.13) Interaction with high stock return .06
(.09)
.24 (.10)
.08 (.10) p-value of test for equality between high and low
executive ownership
.334 .007 .635 p-value of test for equality between high and low
stock returns
.144 .000 .129 Industry Price Coefficients
Coefficient .28 (.14) .44 (.15) .28 (.14) Interaction with low assets -.31
(.13)
-.36 (.15)
-.37 (.14) Interaction with high assets .17
(.12)
.06 (.13)
.25 (.13) p-value of test for equality between high and low
assets
.002 .012 .000
within-firm R2 .092 .136 .096
Regressions estimated over all employees have 5677 observations, middle management regressions have 5647 observations, and top executive regressions have 5128 observations. Regressions are estimated over panel data from 1996-99 obtained from the Execucomp database and various 10-Ks. The basic regression specification is: Log (grant value / employeei,t) = β*Log (pricei,t) + α*Log (industry pricei,t) + firmi + yeart + εi,t
Prices are measured at the beginning of the year. Fourteen industry indices (prices) are constructed over the sample period. The industry groupings are mining, oil and gas, construction, food, basic materials, biotech/medical, manufacturing, transportation, telecom, utilities, retail/wholesale trade, financial, technology, and other.
The coefficient on log price is interacted with whether the average executive ownership share over 1997-98 is in the bottom or top quartile and whether the average stock return over the sample is in the bottom or top quartile of the sample firms. The coefficient on log industry price is interacted with whether firm size (book value of assets averaged over 1997-98) is in the bottom or top quartile of the sample firms.
Table 7:
Regression of Number of Stock Option Grants per Employee on Firm Stock Price 1996-99
(standard errors in parentheses)
All Employees Top Executives
Middle Management Firm Price -.23 (.04) -.39 (.05) -.14 (.05) Regression coefficients when add industry price to the specification
Firm Price -.23 (.04) -.40 (.05) -.15 (.05) Industry Price .08 (.12) .26 (.13) .08 (.13) Regressions estimated over all employees have 5677 observations, middle management regressions have 5647 observations, and top executive regressions have 5128 observations. Regressions are estimated over panel data from 1996-99 obtained from the Execucomp database and various 10-Ks. The basic regression specification is: Log (number of grants / employeei,t) = β*Log (price beginning of yeari,t) + firmi + yeart + εi,t
Prices are measured at the beginning of the year. Fourteen industry indices (prices) are constructed over the sample period. The industry groupings are mining, oil and gas, construction, food, basic materials, biotech/medical, manufacturing, transportation, telecom, utilities, retail/wholesale trade, financial, technology, and other.
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Table 8: Frequency Executive of a Firm is Reported in Proxy Statement 1996-99
(number of times an individual is a “top executive”)
Number of years firm is in sample
Mean number of times executive is mentioned
in proxy statement
Median number of times executive is mentioned
in proxy statement
% of executives who are included in proxy statement every year
1 1 1 100%
2 1.8 2 79
3 2.5 3 65
4 3.0 3 49
Source: Execucomp. There are a total of 2143 different companies from 1996-99: 146 appear in the sample one year, 150 appear two years, 359 appear three years, and 1488 appear all four years.
Table 9: Regression of Stock Option Grant Value per Employee for Low Turnover Firms
(standard errors in parentheses)
All Employees Top Executives
Middle Management Regression coefficients for all S&P 1500 firms
Firm Price .42 (.10) .36 (.11) .53 (.11) Regression coefficients for all S&P 1500 firms
(add Industry Price to specification)
Firm Price .39 (.11) .32 (.11) .51 (.11) Industry Price .48 (.28) .57 (.30) .29 (.31)
To be included in the sample, the firm must have low turnover among its top executives. For firms that are in the sample two years, executives covered by the proxy statement must be reported in the proxy statement both years. For firms that are in the sample three (four) years, executives covered by the proxy statement must be reported in the proxy statement at least three (four) years. The low turnover sample has 1359 firm-year observations.
Regressions are estimated over panel data from 1996-99 obtained from the Execucomp database and various 10-Ks.