- Email: [email protected]
Assessing the utility of executive leadership
ASSESSING THE ~T~~~~ OF EXECUTIVE READERSHIP
Murray R. Barrick* The University
of lowa
David V. Day lou;s;a~a
State U~~vers;ty
Robert G. Lord Ralph A. Alexander The University
of Akron
The financial impact of high-performing executive leaders on organizational performance was determined through the application of a linear decision-theoretic utility procedure. Using archival data from 132 industrial organizations from the Fortune 500 for which data was available over a fifteen year period (1971-1985) in combination with estimates of SDy from a sampie of financial analysts (n = 41), an after-tax utility point estimate averaging over twenty-five million dollars was computed based upon average executive tenure, iU~t~ting the inst~mentatity of a high-mooing executive leader. The discussion noted that the average percent-impact estimate of the financial analysts was almost identical to the average effect size associated with leadership from two often-cited executive succession studies (Lieberson & O’Connor, 1972, Salancik & Pfeffer, 1977). This consistency of impact estimates, across alternative techniques using objective and subjective sources, is contrary to the suggestion that individuals over-attribute organizational performance outcomes to leadership (e.g., Meindl, Ehrlich, & Dukerich, 1985; Meindl & Ehrlich, 1987). An explanation for these conflicting results is framed within the context of the results of prior research on expert/novice differences in decision making.
Over the last decade, the literature regarding the effects of top-level leadership on organizational performance has been characterized by two opposing viewpoints. On one hand, executive leadership has been portrayed as an attributional bias (Calder, 1977; *Direct al1 correspondence to: Murray Iowa, Iowa City, IA 52242.
R. Barrick,
Leadership Quarterly, 2(l), 9-22. Copyright Q 1991 by JAI Press Inc. All rights of reproduction in any form reserved. ISSN: 1048-9843
Coiiege of Business Admi~stration,
The University
of
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Meindl, Ehrlich, & Dukerich, 1985) taking on a “heroic, larger-than-lye quality” (Meindl & Ehrhch, 1987, p. 91) with very little direct in~uence on organizational performance (Pfeffer, 1977). Others, however, have argued that executive leadership is instrumental in reorienting the strategic direction of an organization (Tushman & Romanelli, 1985) and can have a substantial impact on organizational performance (Smith, Carson, & Alexander, 1984; Thomas, 1988; Wiener & Mahoney, 1981). Most studies that have attempted to assess the impact of leadership on organizational performance have investigated the effects of executive succession (e.g., Lieberson & O’Connor, 1972; Salancik & Pfeffer, 1977; Smith et al., 1984; Thomas, 1988; Wiener & Mahoney, 1981). Executive succession studies rely on archival data to determine the extent of changes in the economic performance of an organization following a change in its executive leadership (usually the chief executive officer or chairman of the board). The usual procedure is to then enter the leadership (su~ession) variable into a regression equation along with other relevant organizations and environmental factors. Ultimately, the amount of variance in performance that is explained by each variable is determined. Although there are alternative interpretations of the results of such studies (especially the studies of Lieberson & O’Connor, 1972 and Salancik & Pfeffer, 19771, when carefully interpreted, executive leadership explains 15% to 45% of the variance in relevant organizational outcomes (Day & Lord, 1988) depending upon the dependent variable examined. What has not been determined, however, is the financial value of such a contribution. DEVELOPMENT OF UTILITY ANALYSIS One technique which translates the variance in performance att~butable to leadership into dollars is utility analysis. Utility analysis is the assessment of the economic impact of human resource decisions through the application of mathematical formulate. In the context of the present study, differences in performance among executive leaders reflect personnel decisions and therefore, the impact attributed to this decision can be estimated by means of utility analysis (Landy, Farr, & Jacobs, 1982). Using utility analysis, this study assesses what financial contribution executive leaders make to organizational performance. Utility analysis has been available for years (Brogden, 1949; Cronbach & Gleser, 1965), although only recently has research demonstrated the technique’s practical applicability. Among the most important developments are new procedures to estimate the dollar value of one performance standard deviation (SDy). These procedures have allowed the replacement of complex, expensive, cost-accounting procedures with the use of direct estimates of utility from individuals highly familiar with the job under investigation (Schmidt, Hunter, McKenzie, & Muldrow, 1979). Other developments have been the incorporation of capital budgeting techniques (Alexander & Cronshaw, 1984; Cronshaw & Alexander, 1985), economic concepts (Boudreau 1983a; 1983b), and risk analysis (Alexander & Barrick, 1987; Alexander, Cronshaw, & Barrick, 1986; Cronshaw, Alexander, Wiesner, & Barrick, 1987; Rich & Boudreau, 1987) to provide a more comprehensive utility estimate. These estimates recognize certain basic economic realities. For instance, traditional utility equations report a single point estimate of the value of au intervention. Such an estimate, while
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providing an average value, does not provide a standard error for these estimates, and therefore provides an incomplete measure of the intervention’s expected utility. One technique-referred to as risk analysis-incorporates not only the expected value of the intervention but also the standard error of these estimates (Alexander & Barrick, 1987). This risk analysis utility estimate clarifies the extent of uncertainty associated with each utility point estimate. Another conceptual development extends the utility procedure from traditional selection procedures to any human resource intervention (Landy et al., 1982). Adopting the latter approach, the present study investigates the value of executive leadership using a decision-theoretic utility equation. Integrating these developments, the utility equation adopted in this study will provide a more useful financial estimate of executive utility. The equation (Boudreau, 1983a) is stated as:
U=(l+v-l
(SDy)(d*)(l
-
TR) - (G)( 1 -
7X)
i(1 + i)r where U = leader utility or the financial contribution attributed to executive leadership for the average organization; i = the cost of capital (also called the discount rate); T = the average tenure of executive leaders; SDy = the value of one standard deviation of executive job performance expressed in dollars; 14 = a standardized effect size, or the mean standard score difference in job performance between high-performing executives and other executives; TR = tax rates; and C, = all cash outlays associated with implementing the intervention, which in this study primarily reflects recruiting costs. In this equation, the variables preceding the cash outlays variable (except U) represent parameters from the returns component, while the remaining variables are from the cost component. In addition to demonstrating an expected utility point estimate for executive leadership, this study will establish the extent of variability inherent in these potential dollar gains by investigating the influence of the uncertainty associated with each parameter estimate. Alexander and Barrick (1987) have shown that the variance of the composite function of variables in the utility equation can be estimated by incorporating the standard error of each variable measured. The equation to account for the variability attributed to this uncertainty is expressed as:
where S,,’ is the variance of the overall utility estimate U, ri and cj represent variables i and j associated with the returns and cost components, respectively; s,? and S,* establish the extent of variance or uncertainty associated with the ith andjth variables; Mr? and MCI2are the squared mean values of the ith and jth variables; and nri and n, reflect the ith andjth variables sample size. In summary, the present study will assess whether financial value is provided by executive leadership and investigate the extent of variability associated with this
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assessment. As this suggests, executive leaders identified as high performers should not only have a larger impact on organizational performance than mediocre-performing executives, but the lower 95% confidence bound for this single utility point estimate should be greater than zero. This approach corresponds to previous studies that have demonstrated the importance of differentiating effective from other leaders in accurately assessing the impact of leadership on performance (Pfeffer & Davis-Blake, 1986; Smith et al., 1984).
METHOD Sample and Procedure
This analysis is based upon the data from one hundred and thirty-two industrial organizations from the Fortune 500 for which data was available over a 15 year period (1971-1985). In this study, the sample size was restricted by the fact that the analysis was limited to industrial organizations that had remained in the Fortune 500 list for 15 consecutive years. Because we wished to maximize our sample size (and the subsequent power of the analyses), we did not subgroup the organizations. However, all organizations sampled fit into the category of general manufacturing, falling under nineteen consecutive, two-digit standard industrial classification codes (i.e., 20 to 39). This sampling procedure is consistent with previous studies of executive leadership (e.g., Wiener & Mahoney, 1981). All parameter data, except for SDy, were obtained through an archival search, primarily utilizing Forbes and Value Line Investment Surveys’. To obtain SDy, one hundred and fifty financial analysts were mailed a questionnaire that adopted the Schmidt et al. (1979) judgmental technique for producing SDy judgments. Financial analysts were selected because theirjob entails making a projection of corporate performance. These projections take into account, usually implicitly, the extent to which managerial ability will influence future corporate outcomes. Forty-one usable questionnaires were returned (27.3%), which is a reasonable return rate for mail survey methodology (Lansing & Morgan, 1971). The sample of financial analysts was obtained from listings in The Financial Analysts Federation Membership Directory, where these analysts were members of the Columbus, Cleveland, or Des Moines chapters (due to their proximity to the authors) and were identified as experts in the manufacturing sector. Parameter
For this analysis (z] was estimated
Estimates
at the conventional
level of 10%.
Tenure This estimate reflects the average length of tenure for those executives that withdraw from the CEO position, for whatever reason, during the 15 year period of analysis. Data were recorded as presented in Forbes.
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Leader Effect Size
The effect-size (dl) parameter reports the magnitude of leader effects on organizational performance based upon the standardized mean differences between high-performing and other leaders. Executive leaders were differentiated into high performers and others based upon total annual remuneration. This measure poses some problems (see Kerr & Bettis, 1987, for a recent discussion of this literature) though it is has been demonstrated (Lewellan & Huntsman, 1970; Murphy, 1985; Seligman, 1984; Smith et al., 1984) that chief executive pay is associated with higher firm performance. Therefore executive compensation was selected as the best available proxy for reflecting the impact executives have upon their organizations. The problem with the choice of compensation as a proxy for executive performance is that it could be confounded by other factors that have previously been demonstrated to be related to executive compensation. Among these factors are the employers’ ability to pay and the executive’s human capital assets (Agarwal, 1981; Gomez-Mejia, Tosi, & Hinkin, 1987; Selgiman, 1984). To reduce this confounding effect of other factors, compensation was regressed onto (1) the firm’s ability to pay and (2) human capital assets. The employer’s ability to pay is reflected in two measures; the relative size of the organization (measured by total assets), and the relative value of the firm (measured by the firm’s annual market value). The executive’s human capital assets were assessed by the executive’s experience or tenure (measured by the number of years as CEO) and whether the executive was an internal or external selection (where CEOs hired from outside the company were coded as “1” and those hired internally were coded as “0”). All these variables were obtained from Forbes. The resulting residual scores thus remove the variance attributed to these factors from the executive’s compensation and may be construed as a proxy measure of the premium that the board of directors is willing to pay the executives in order to reward them for being an effective leader.’ That is, high performing leaders were identified as those receiving the highest residual salaries (adjusted to 1985 dollars, see note 1) consistently across their careers during the 15 year period (i.e., 1971-1985). In this study, three distinct contrasts were conducted where high performing leaders are defined as those leaders receiving the top lo%, 20%, and 30% of residualized total remuneration. In these analyses, the high performers were dummy coded with a 1 and all other executives were coded 0. The standardized leader effect size (d,) was computed with the formula suggested by Landy et al. (1982, pg. 27):
where Mnpe= the mean performance of the high-performing executive; MO, = the mean performance of other executives; and SD = the pooled within-group standard deviation3 Three separate measures were selected as the financial measures of corporate performance: earnings per share, return on equity and net income. These measures were selected because they provide a relatively comprehensive picture of organizational success (Joy, 1977; Pringle & Harris, 1984).
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In summary, total remuneration, adjusted for the employer’s ability to pay and the executive’s human capital assets, was expected to reflect the leader’s competence, as perceived by the board of directors. High performing leaders are variously defined as those leaders scoring in the top lo%, 20$?&or 30% of the sampled executives. The effectsize (~4) parameter utilizes this distinction between high performers and other leaders to depict the standardized difference in job performance between the average person in the high performing group and the average person in the mediocre group across the three performance measures. For each performance variable, the average value for either group is based upon the sum of the annual performance values (adjusted to 1985 dollars, see note 1) during the executive’s tenure divided by the total tenure of those executives over the 15 year period (i.e., 1971-1985) assessed in this study. Tax Rate This parameter is based upon the average tax rate reported by the organizations the 15 year period as reported in Value Line Investment Surveys.
over
Cost Fstimates Although a search of the executive recruiting literature failed to reveal the necessary information, informal discussions with professional executive recruiters led to the conclusion that one-third of an executives’ first year cash salary is an effective estimate for these recruiting costs.4 Estimating SDy For the present study, two modifications of the Schmidt et al. (1979) procedure were necessary. First, SDy estimates were based upon a single position across multiple organizations, rather than within one organization. Nevertheless, financial analysts regularly make such comparisons across organizations, so it was expected that they could provide a useful estimate of the value of different levels of leader effectiveness. A recent study (Schmidt, Hunter, Outerbridge, & Trattner, 1986) used such a crossorganizational approach to generalize effects to the entire Federal government. Second, dollar estimates were not requested from the financial analysis because the executives were from organizations of considerably different sizes. These size differences make a comparison of dollar-based, performance-related estimates of executive influence uninterpretable. Instead, a percent estimate of the executive’s impact upon net income was requested because it is a metric that is not confounded by organizational size. These estimates were converted into dollar values for the sample as follows: The analysts’ mean SDy percent estimates was multiplied with the sample’s average net income, where net income data was obtained from Forbes. In addition to this mean SDy value, a variance estimate expressed in dollars, was obtained by multiplying the standard error of these SDy estimates with average net income. To obtain this estimate, the analysts were informed that the purpose of their ratings was to provide an estimate of the impact that manufacturing executives (from the Fortune 500) would have upon an organization’s total net income. Ratings were requested for three different levels of executive performance; at the 15th (i.e., low performers), 50th (i.e., average performers), and 85th (i.e., superior performers) percentile. After these different performance levels were described, the financial analysts
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were asked to provide a percent estimate for the various performance following manner:
levels in the
Based on my experience I estimate the impact of an average (low performing, superior performing) executive on the net income of a Fortune 500 industrial company at ~ percent per year. (Please estimate to the nearest one-tenth of a percentile).
These percentile estimates, combined with average net income, provided 15th, 50th, and 85th percentile performance-equivalent dollar estimates. When such estimates are normally distributed, one can average SDyl (50th-15th) and SDy2 (85th-50th) to determine an overall SDy estimate (Schmidt et al., 1979). RESULTS The effect-size estimates (dJ for each dependent variable for the three separate analyses of high-performing executive samples are reported in Table 1. As expected, the highperforming groups mean values increased as the analyses progressed from the top 30% of high-performing executives to the more exclusive top 10% sample across all three organizational measures. Nevertheless, the actual effect-size estimates generally did not vary much and tended to converge on effect-size values in the low 20s.’ Therefore, for purposes of discussion we will focus on the return on equity value reported for the top 20% of high-performing executives as a representative value across the three organizational measures. All other parameter estimates are reported in Table 2.
Table 1 Average Effect Size Estimates for Three High-Performing Samples of Executives Across Three Organizational Measures Variables
New Income Earnings Per Share Return on Equity
Hi- Perf Mean
Other Mean
d,
Standard Error
N’
(Top 10%)
(Other 90%) 426.61 3.454 .218
.198 ,352 ,223
.0771 .0762 .0779
190 190 182
4a4.475= 4.126 .246 (Top
20%)
Net Income Earnings Per Share Return on Equity
473.09 = 4.589 .240
410.907 3.44 .216
,193 ,307 ,219
.0567 .0562 .0571
392 392 380
Net Income Earnings per Share Return on Equity
(Top 30%) 501.659’ 4.145 .243
(Other 70%) 448.52 3.509 ,221
,163 ,157 ,172
.0490 .0486 .0496
600
Notes:
600 575
I N-sizes depict the number of years data was available for the top lo%, top 20%, or top 30% subsamples (Note, total N-size = 1,980). N-sizes within a particular subsample (e.g., top IO%), vary across the four organizational measures due to missing values. ’ Reported in thousands of dollars.
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Descriptive Variable
i T SDy6 TR c0
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Table 2 Statistics of Parameter Estimates
Mean (M)
Std. Error (s)
Size (n)
.lO” 10.344 $30,405,000 ,388 $171,311
NA so7 $5,088,000 .00309 $6,820
NA 220 41 2032 209
Notes: Where the variables are: i = cost of capital or discount rate, which in this study is assumed to remain constant; T = average tenure of executive leaders; SDy = dollar value of one performance standard deviation; TR = tax rates; and Co = cash outlays required to retain the intervention. ’ This value was selected as an “average” discount rate over the 15-year period of analysis (1971-1985). It should be noted that an increase of 5 percent (i.e., to .15) would result in a 19% reduction in the overall utility estimate. b SOY entries are based on the product of the financial analysts’judgments and net income (as reported in Forbes).
Table 3 Mean and 95% Confidence Interval Values for Three High-Performing Executives Across Three Organizational Measures Variables
Mean”
Upper Bout&
Samples of
Lower Boun&
Net Income Earnings Per Share Return on Equity
Top 10% of Executives vs. the other 90% $41,566 $22,959 $40,898 $63,116 $25,871 $44,964
$4,353 $18,680 $6,779
Net Income Earnings Per Share Return on equity
Top 20% of Executives vs. the other 80% $22,377 $40,892 $56,681 $35,656 $44,417 $25,405
$3,862 $14,632 $6,393
Net Income Earnings Per Share Return on Equity
Top 30% of Executives vs. the other 70% $18,882 $36,888 $18,183 $36,096 $38,081 $19,931
$876 270 $1,779
’ Reported in thousands
of dollars.
The SDy value reported in this study reflects the product of the financial analysts’ percent-impact estimates and the average net income of the organizational sample, as reported in Forbes. Utilizing the Schmidt et al. (1979) procedure for the financial analysts’judgments, we computed a value of 14.917% (std. error = 17.804%) for SDyl (i.e., 50-15) and 15.295% (std. error = 14.595% for SDy2 (i.e., 85-50). The average value between these estimates was 15.106% (std. error = 2.528%) and the average net income, based upon the sample from Forbes, was $201,278,000. Therefore, the overall mean value of one standard deviation in executive job performance, expressed in dollars, is the product of the terms or $30,405,000 (std. error = $5,088,000). Applying the utility equation (equation 1) to the various parameters’ mean values reported in Table 2 with the return on equity effect-size estimate for the top 20% of high performing executives (as reported in Table I), resulted in utility point estimate (U) of $25,405,417 over an executive’s average tenure (after accounting for taxes). This
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suggests that an effective leader contributes a significant financial benefit to an average Fortune 500 manufacturing company. Finally, solving for equation 2 provides an assessment of the impact attributed to the uncertainty of these parameters. This analysis demonstrates that the 95% confidence interval around the utility point estimate ($6,393,468 L $25405,407 5 $44, 417, 365) exceeds the breakeven value, further illustrating the instrumentality of a high-performing executive. It should be noted that these results are based upon an effect size of .22. Nevertheless, effect size estimates from the other organizational measures generally resulted in comparable outcomes, with mean values ranging from 18 million to 41 million dollars as reported in Table 3. The lower confidence bound ranged from $270,096 to over $18 million, excluding zero in all cases, thus demonstrating the positive utility of a high-performing executive to an organization.
DISCUSSION This study estimates the value of high performing executives through the application of a linear decision-theoretic utility procedure. The results indicate that a highperforming executive has a substantial impact (in terms of dollars) on the economic performance of a Fortune 500 company. It is interesting to note that the average percentimpact estimates (SDy values) of the financial analysts (. 15 1) in this study are consistent with the average leadership effect sizes computed from archival sources by Lieberson and O’Connor (1972) and Salancik and Pfeffer (1977). Across all economic variables in their respective studies, Lieberson and O’Connor’s data show an average effect size of. 148 for administration (leadership) succession with an appropriate (3-year) time lag. Salancik and Pfeffer’s data yields an average effect size of .161 across all budget categories, when computed as proportions of the total city budget.6 Though it is true that the average effect-size from the executive leadership literature is based on a limited number of studies, many critiques regarding the relatively small effect of leadership on organizational performance have been made using these same studies and effectsizes (see Day & Lord, 1988, for a discussion of this issue). This consistency across the objective and subjective estimates of an executive leader’s effect on organizational performance has important implications for the notion that leadership is a heroic, over-used concept. The consistency between our results and those from the executive succession studies is contrary to the recent conclusions of Meindl and his associates (Meindl et al., 1985; Meindl & Ehrlich, 1987). Using samples of undergraduate and graduate students who were novice estimators of leader impact, they concluded that performance outcomes were over-attributed to leadership. The present study, using judgments of experts familiar with the extent of executive impact (i.e., financial analysts), found that the average estimate judgments correspond to independently derived, objective statistical results. This suggests that a heroic, romanticized explanatory concept may be used by individuals who are less sophisticated, novice judges of leadership effects. Expert/ Novice Differences
That experts and novices Research has demonstrated
should differ in their respective that expert/novice judgments
judgments is expected. often differ within a
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particular knowledge domain (e.g., Chi, Glaser, & Rees, 1982; Fredrickson, 1985; Larkin, McDermott, Simon, & Simon, 1980). There is a relatively straightforward explanation for the differences in results between our study and those of Meindl and associates (Meindl et al., 1985; Meindl & Ehrlich, 1987). Experts provide more accurate judgments-whether it is estimating the financial impact of high-performing executives or generating solutions to physics problems (Chi et al., 1982)-because they can rely on extensive, context-specific knowledge structures to process information (Galambos, Abelson, & Black, 1986). On the other hand, novices are more likely to use more general attributional procedures based on a leader’s general salience (Taylor & Fiske, 1978) since they lack detailed and organized, context-specific information. Therefore, despite attempts to reduce the salience level of a leader in written vignettes (Meindl et al., 1985, Study 6), it should come as no surprise that novices attribute a higher degree of control to leadership simply because they do not know the detailed contextual framework necessary for considering the effects of other factors. Thus financial analysts, experts in this domain, have probably observed numerous succession events of chief executives, and the extent of such successions’ impact on organizational performance. Therefore, the consistency between financial analysts’ SDy judgments and objectively-derived values suggests that financial analysts can be quite accurate and capable of incorporating this knowledge of succession effects. Executive leader Utility The results from this study suggest that a high-performing executive’s impact on firm performance would be at least 15 percent higher than the average-performing executive. The value of these performance differences is expected to exceed 25 million dollars (after taxes) for a Fortune 500 company. It should be noted that the computed dollar values are representative only of those large, manufacturing Fortune 500 organizations sampled in the present study, although such effects on corporate performance are relevant and crucial to smaller organizations. Moreover, because top-level leaders in small organizations have a generally large latitude of action (Hambrick & Finkelstein, 1987), it might be argued that this percent estimate is too low for these organizations because their executive leaders have a much greater effect on the performance of their organizations. The significance of the results from the present study are further underscored by the fact that it is entirely possible that high-performing executives would have no utility. For example, the financial analysts could have said they had little impact, giving us a SDy estimate around zero. In addition, and most convincing, there may have been no effect (i.e., no mean difference in organizational performance) between highly compensated and other executives. Although the first possibility could reflect perceptual bias, the second could not. In fact, as the results from a recent study suggest (Tosi & Gomez-Mejia, 1989), executive compensation would be expected to be a better indicator of leader performance in owner-controlled than management-controlled firms. Therefore, our effect size (d,) estimate would likely have been larger if we had been able to restrict our sample to owner-controlled firms. However, since our study presumably included both types of firms, the relationship between executive compensation and performance resulted in a weaker effect size estimate than would have occurred in a sample of owner-controlled firms.
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Nevertheless, there remains the issue of executive compensation and the possibility of reverse causality. That is, executives may be highly-compensated for good organizational performance, when in fact they had no impact on that performance. Although this interpretation cannot be dismissed, there are arguments that contradict it. First, in this study, executive pay was regressed onto firm performance (i.e., market value) prior to using executive compensation as a proxy for leader performance. Second, this reverse causality supposition conflicts with the estimates of executive impact provided by financial analysts who are familiar with succession events. Third, succession studies, which are not subject to such reverse causality effects, find that the amount of variance associated with succession is very similar to the SDy estimates of our expert financial analysts. In addition, succession studies have found greater effects for high as compared to average pe~o~ing leaders both when this distinction was based on salary (Smith et al., 1984) and when it was based on past performance (Pfeffer & DavisBlake, 1986). These alternative means of differentiating high versus average performing leaders produced very similar results. This issue actually is one facet of the larger debate over the causal relationship between leader behavior and organizational performance. Even though leadership does not explain a majority of the variance in organizational performance, it should not be expected to do so. Further, although the average effect size for leadership is relatively small (generally 20%-25%, with approximateiy 75%80% of the variance in performance being attributed to other factors), the financial impact of effective leadership is substantial-an average estimate of over 25 million dollars (after taxes) throughout an executive’s average career span. This is hardly trivial. Practical Implications
This last point underscores the important practical implications of the results of our study pertaining to two specific areas: (1) selection/succession of top-level executives, and (2) executive development. If the average impact of effective leadership of more than 25 million dollars over the average tenure for an executive is confirmed in future research, it argues that the considerable amount of time and money spent by organizations in recruiting and succession planning at the top executive levels is a sound investment. Our results also imply that developing or “grooming” individuals for top-level positions could help an organization maximize its leadership effectiveness and organizations pe~o~ance. This requires a cla~cation of the specific knowledge or abilities possessed by high-pe~orming executives. Although there has yet to be a systematic investigation identifying the means by which top-level executives influence organizational performance, there is some work which provides some direction concerning methodological considerations and types of executive actions that may be most influential (e.g., Day & Lord, 1988; Hambrick & Finkelstein, 1987; Tushman & Romanelli, 1985). NOTES 1. To ensure that all variables were measured in dollars of equal value across the various years (19’71-1985),all dollar values were adjusted to 1985 dollars based upon the Consumer Price Index as reported in ~o~r~~y Labor Review.
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2. We thank one of the reviewers for the suggestion that the compensation residual, after controlling for other variables that affect compensation, could represent the premium the CEO receives because he/she has been judged to be competent by the board of directors. That is, we assume that the directors have attempted to control executive behavior by making compensation and termination decisons based on their perceptions of firm performance. 3. In this study, the standard error of the true effect size (S) was estimated by applying the large-sample approximation suggested by Hedges (1982). 4. Other cost considerations include variable compensation costs that are based on organizational performance. While those variable costs could impact on the utility estimate, they do not substantially affect the outcome of the present study, Therefore, variable costs are not included in order to keep from complicating the issue any further. 5. The effect sizes (d,) were also calculated using year to year changes in the performance variables (available upon request from the first author). The resulting effect-size estimates were roughly equivalent to those based on overall averages (see Table 1). 6. These figures are available in the original sources or can be found in Day and Lord (1988, Table 1).
REFERENCES Agarwal, N. (1981). Determinants of executive compensation. Industrial Relations, 20, 36-45. Alexander, R. A., & Barrick, M. R. (1987). Estimating the standard error of projected dollar gains in utility analysis. Journal of Applied Psychology, 71, 475-479. Alexander, R. A., & Cronshaw, S. F. (1984). The utility of selection programs: Afinance-based perspective. Paper presented at the 92nd Annual Convention of the American Psychological Association, Toronto, Canada. Alexander, R. A., Cronshaw, S. F., & Barrick, M. R. (1986). Extending the managerialfinance model of utility analysis to deal with uncertainty in parameter estimates. Paper presented at the 1st Annual Conference of the Society for Industrial and Organizational Psychology, Chicago, IL. Boudreau, J. (1983a). Effects of employee flows on utility analysis of human resource productivity improvement programs. Journal of Applied Psychology, 68, 396-406. Boudreau, J. (1983b). Economic considerations in estimating the utility of human resource productivity improvement programs. Personnel Psychology, 36, 55 l-576. Brogden, H. E. (1949). When testing pays off. Personnel Psychology, 2, 171-183. Calder, B. J. (1977). An attribution theory of leadership. In B. M. Staw and G. R. Salancik (Eds.), New directions in organizational behavior (pp. 179-204). Chicago: St. Clair. Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence, I(pp. 7-75). Hillsdale, NJ: Erlbaum. Cronbach, L. J., & Gleser, G. C. (1965). Psychological tests andpersonnel decisions. Urbana, IL: University of Illinois Press. Cronshaw, S. F., & Alexander, R. A: (1983). The selection utility model as an investment decision: The greening of selection utility. Proceedings of the 43rd Annual Meeting of the Academy of Management, 297-300. Cronshaw, S. F., & Alexander, R. A. (1985). One answer to the demand for accountability: Selection utility as an investment decision. Organizational Behavior and Human Decision Processes, 35, 102-l 18. Cronshaw, S. F., Alexander, R. A., Wiesner, W., & Barrick, M. R. (1987). Incorporating risk into selection utility: Two models for sensitivity analysis and risk simulation. Organizational Behavior and Human Decision Processes, 40, 270-286.
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Day, D. V., & Lord, R. G. (1988). Executive leadership and organizational performance: Suggestions for a new theory and methodology. Journal of Management, 14, 453-464. Financial Analysts Federation (1986). The financial analysts federation membership directory. New York, NY. Forbes. (1972-1986). The dimensions of American busineses: Ranking the Forbes “500”. New York, NY: Forbes, Inc. Fredrickson, J. W. (1985). Effects of decision motive and organizational performance level on strategic decision processes. Academy of Management Journal, 28, 821-843. Galambos, J. A., Abelson, R. P., & Black, J. B. (1986). Knowledge structures. Hillsdale, NJ: Erlbaum. Gomez-Mejia, L. R., Tosi, H. L., & Hinkin, T. (1987). Managerial control, performance and executive compensation. Academy of Management Journal, 30, 51-70. Hambrick, D. C., & Finkelstein, S. (1987). Managerial discretion: A bridge between polar views of organizational outcomes. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behavior (Vol. 9, pp. 369-406). Greenwich, CT: JAI Press. Hedges, L. V. (1982). Estimation of effect size from a series of independent experiments. Psychological Bulletin, 92, 490-499. Joy, 0. M. (1977). Introduction tofinancial management. Homewood, IL: Irwin Dorsey. Kerr, J., & Bettis, R. A. (1987). Boards of directors, top management compensation, and shareholder returns. Academy of Management Journal, 30, 645-664. Landy, F. J., Farr, J. L., & Jacobs, R. R. (1982). Utility concepts in performance management. Organizational Behavior and Human Performance, 30, 15-40. Lansing, J. B., & Morgan, J. N. (1971). Economic survey methods. Institute for Social Research, University of Michigan, Ann Arbor. Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342. Lewellan, W., Jr Huntsman, B. (1970). Managerial pay and corporate performance. American Economic Review, 60, 710-720. Lieberson, S., & O’Connor, J. F. (1972). Leadership and organizational performance: A study of large corporations. American Sociological Review, 37, 117-130. Meindl, J. H., & Ehrlich, S. B., & Dukerich, J. M. (1985). The romance of leadership. Administrative Science Quarterly, 30, 78-102. Meindl, J. H., & Ehrlich, S. B. (1987). The romance of leadership and the evaluation of organizational performance. Academy of Management Journal, 30, 91-109. Monthly Labor Review. Washington, DC: U.S. Government Printing Office. Murphy, K. J. (1985). Corporate performance and managerial remuneration. Journal of Accounting and Economics, 7, 1 l-42. Pfeffer, J. (1977). The ambiguity of leadership. Academy of Management Review, 2, 104-l 12. Pfeffer, J., & Davis-Blake, A. (1986). Administrative succession and organizational performance: How administrator experience mediates the succession effect. Academy of Management Journal, 29, 72-83. Pringle, J. J., & Harris, R. S. (1984). Essentials of managerial finance. Glenview, IL: Scott Foresman and Company. Rich, J. R., & Boudreau, J. W. (1987). The effects of variability and risk in selection utility analysis: An empirical comparison. Personnel Psychology, 40, 55-84. Salancik, G. H., & Pfeffer, J. (1977). Constraints on administrator discretion: The limited influence of mayors on city budgets. Urban Affairs Quarterly, 12, 475-498. Schmidt, F. L., Hunter, J. E., McKenzie, R. C., & Muldrow, T. W. (1979). Impact of valid selection procedures on work-force productivity. Journal of Applied Psychology, 64, 609626.
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Schmidt, F. L., Hunter, J. E., Outerbridge, A. N., & Trattner, M. H. (1986). The economic impact ofjob selection methods on size, productivity, and payroll costs of the Federal Work Force: An empirically based demonstration. Personnel Psychlogy, 39, l-30. Seligman, D. (1984, June 11). Believe it or not, top-executive pay may make sense. Fortune, 57-60.
Smith, J. E., Carson, K. P., & Alexander, R. A. (1984). Leadership: It can make a difference. Academy of Management
Journal, 27, 765-776.
Taylor, S. E., & Fiske, S. T. (1978). Salience, attention, and attribution: Top of the head phenomena. In L. Berkowitz (Ed.), Advances in experimental social psychology, 2 (pp. 249-288). Orlando, FL: Academic Press. Thomas, A. B. (1988). Does leadership make a difference to organizational performance? Administrative
Science Quarterly, 33, 388400.
Tosi, H. L. Jr., & Gomez-Mejia, L. R. (1989). The decoupling of CEO pay and performance: An agency theory perspective. Administrative Science Quarterly, 34, 169-189. Tushman, M. L., & Romanelli, E. (1985). Organizational evolution: A metamorphism model of convergence and reorientation. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behavior, 7 (pp. 171-222). Greenwich, CT: JAI Press. 77re Value Line Investment Survey. (1972-1986). New York, NY: Arnold Bernhard & Co., Inc. U.S. Department of Labor, Bureau of Labor Statistics. Wiener, N., & Mahoney, T. A. (1981). A model of corporate performance as a function of environmental, organizational, and leadership influences. Academy of Management Journal, 24, 453-470.
Murray R. Barrick* The University
of lowa
David V. Day lou;s;a~a
State U~~vers;ty
Robert G. Lord Ralph A. Alexander The University
of Akron
The financial impact of high-performing executive leaders on organizational performance was determined through the application of a linear decision-theoretic utility procedure. Using archival data from 132 industrial organizations from the Fortune 500 for which data was available over a fifteen year period (1971-1985) in combination with estimates of SDy from a sampie of financial analysts (n = 41), an after-tax utility point estimate averaging over twenty-five million dollars was computed based upon average executive tenure, iU~t~ting the inst~mentatity of a high-mooing executive leader. The discussion noted that the average percent-impact estimate of the financial analysts was almost identical to the average effect size associated with leadership from two often-cited executive succession studies (Lieberson & O’Connor, 1972, Salancik & Pfeffer, 1977). This consistency of impact estimates, across alternative techniques using objective and subjective sources, is contrary to the suggestion that individuals over-attribute organizational performance outcomes to leadership (e.g., Meindl, Ehrlich, & Dukerich, 1985; Meindl & Ehrlich, 1987). An explanation for these conflicting results is framed within the context of the results of prior research on expert/novice differences in decision making.
Over the last decade, the literature regarding the effects of top-level leadership on organizational performance has been characterized by two opposing viewpoints. On one hand, executive leadership has been portrayed as an attributional bias (Calder, 1977; *Direct al1 correspondence to: Murray Iowa, Iowa City, IA 52242.
R. Barrick,
Leadership Quarterly, 2(l), 9-22. Copyright Q 1991 by JAI Press Inc. All rights of reproduction in any form reserved. ISSN: 1048-9843
Coiiege of Business Admi~stration,
The University
of
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Meindl, Ehrlich, & Dukerich, 1985) taking on a “heroic, larger-than-lye quality” (Meindl & Ehrhch, 1987, p. 91) with very little direct in~uence on organizational performance (Pfeffer, 1977). Others, however, have argued that executive leadership is instrumental in reorienting the strategic direction of an organization (Tushman & Romanelli, 1985) and can have a substantial impact on organizational performance (Smith, Carson, & Alexander, 1984; Thomas, 1988; Wiener & Mahoney, 1981). Most studies that have attempted to assess the impact of leadership on organizational performance have investigated the effects of executive succession (e.g., Lieberson & O’Connor, 1972; Salancik & Pfeffer, 1977; Smith et al., 1984; Thomas, 1988; Wiener & Mahoney, 1981). Executive succession studies rely on archival data to determine the extent of changes in the economic performance of an organization following a change in its executive leadership (usually the chief executive officer or chairman of the board). The usual procedure is to then enter the leadership (su~ession) variable into a regression equation along with other relevant organizations and environmental factors. Ultimately, the amount of variance in performance that is explained by each variable is determined. Although there are alternative interpretations of the results of such studies (especially the studies of Lieberson & O’Connor, 1972 and Salancik & Pfeffer, 19771, when carefully interpreted, executive leadership explains 15% to 45% of the variance in relevant organizational outcomes (Day & Lord, 1988) depending upon the dependent variable examined. What has not been determined, however, is the financial value of such a contribution. DEVELOPMENT OF UTILITY ANALYSIS One technique which translates the variance in performance att~butable to leadership into dollars is utility analysis. Utility analysis is the assessment of the economic impact of human resource decisions through the application of mathematical formulate. In the context of the present study, differences in performance among executive leaders reflect personnel decisions and therefore, the impact attributed to this decision can be estimated by means of utility analysis (Landy, Farr, & Jacobs, 1982). Using utility analysis, this study assesses what financial contribution executive leaders make to organizational performance. Utility analysis has been available for years (Brogden, 1949; Cronbach & Gleser, 1965), although only recently has research demonstrated the technique’s practical applicability. Among the most important developments are new procedures to estimate the dollar value of one performance standard deviation (SDy). These procedures have allowed the replacement of complex, expensive, cost-accounting procedures with the use of direct estimates of utility from individuals highly familiar with the job under investigation (Schmidt, Hunter, McKenzie, & Muldrow, 1979). Other developments have been the incorporation of capital budgeting techniques (Alexander & Cronshaw, 1984; Cronshaw & Alexander, 1985), economic concepts (Boudreau 1983a; 1983b), and risk analysis (Alexander & Barrick, 1987; Alexander, Cronshaw, & Barrick, 1986; Cronshaw, Alexander, Wiesner, & Barrick, 1987; Rich & Boudreau, 1987) to provide a more comprehensive utility estimate. These estimates recognize certain basic economic realities. For instance, traditional utility equations report a single point estimate of the value of au intervention. Such an estimate, while
Executive
11
Leadership
providing an average value, does not provide a standard error for these estimates, and therefore provides an incomplete measure of the intervention’s expected utility. One technique-referred to as risk analysis-incorporates not only the expected value of the intervention but also the standard error of these estimates (Alexander & Barrick, 1987). This risk analysis utility estimate clarifies the extent of uncertainty associated with each utility point estimate. Another conceptual development extends the utility procedure from traditional selection procedures to any human resource intervention (Landy et al., 1982). Adopting the latter approach, the present study investigates the value of executive leadership using a decision-theoretic utility equation. Integrating these developments, the utility equation adopted in this study will provide a more useful financial estimate of executive utility. The equation (Boudreau, 1983a) is stated as:
U=(l+v-l
(SDy)(d*)(l
-
TR) - (G)( 1 -
7X)
i(1 + i)r where U = leader utility or the financial contribution attributed to executive leadership for the average organization; i = the cost of capital (also called the discount rate); T = the average tenure of executive leaders; SDy = the value of one standard deviation of executive job performance expressed in dollars; 14 = a standardized effect size, or the mean standard score difference in job performance between high-performing executives and other executives; TR = tax rates; and C, = all cash outlays associated with implementing the intervention, which in this study primarily reflects recruiting costs. In this equation, the variables preceding the cash outlays variable (except U) represent parameters from the returns component, while the remaining variables are from the cost component. In addition to demonstrating an expected utility point estimate for executive leadership, this study will establish the extent of variability inherent in these potential dollar gains by investigating the influence of the uncertainty associated with each parameter estimate. Alexander and Barrick (1987) have shown that the variance of the composite function of variables in the utility equation can be estimated by incorporating the standard error of each variable measured. The equation to account for the variability attributed to this uncertainty is expressed as:
where S,,’ is the variance of the overall utility estimate U, ri and cj represent variables i and j associated with the returns and cost components, respectively; s,? and S,* establish the extent of variance or uncertainty associated with the ith andjth variables; Mr? and MCI2are the squared mean values of the ith and jth variables; and nri and n, reflect the ith andjth variables sample size. In summary, the present study will assess whether financial value is provided by executive leadership and investigate the extent of variability associated with this
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assessment. As this suggests, executive leaders identified as high performers should not only have a larger impact on organizational performance than mediocre-performing executives, but the lower 95% confidence bound for this single utility point estimate should be greater than zero. This approach corresponds to previous studies that have demonstrated the importance of differentiating effective from other leaders in accurately assessing the impact of leadership on performance (Pfeffer & Davis-Blake, 1986; Smith et al., 1984).
METHOD Sample and Procedure
This analysis is based upon the data from one hundred and thirty-two industrial organizations from the Fortune 500 for which data was available over a 15 year period (1971-1985). In this study, the sample size was restricted by the fact that the analysis was limited to industrial organizations that had remained in the Fortune 500 list for 15 consecutive years. Because we wished to maximize our sample size (and the subsequent power of the analyses), we did not subgroup the organizations. However, all organizations sampled fit into the category of general manufacturing, falling under nineteen consecutive, two-digit standard industrial classification codes (i.e., 20 to 39). This sampling procedure is consistent with previous studies of executive leadership (e.g., Wiener & Mahoney, 1981). All parameter data, except for SDy, were obtained through an archival search, primarily utilizing Forbes and Value Line Investment Surveys’. To obtain SDy, one hundred and fifty financial analysts were mailed a questionnaire that adopted the Schmidt et al. (1979) judgmental technique for producing SDy judgments. Financial analysts were selected because theirjob entails making a projection of corporate performance. These projections take into account, usually implicitly, the extent to which managerial ability will influence future corporate outcomes. Forty-one usable questionnaires were returned (27.3%), which is a reasonable return rate for mail survey methodology (Lansing & Morgan, 1971). The sample of financial analysts was obtained from listings in The Financial Analysts Federation Membership Directory, where these analysts were members of the Columbus, Cleveland, or Des Moines chapters (due to their proximity to the authors) and were identified as experts in the manufacturing sector. Parameter
For this analysis (z] was estimated
Estimates
at the conventional
level of 10%.
Tenure This estimate reflects the average length of tenure for those executives that withdraw from the CEO position, for whatever reason, during the 15 year period of analysis. Data were recorded as presented in Forbes.
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13
Leader Effect Size
The effect-size (dl) parameter reports the magnitude of leader effects on organizational performance based upon the standardized mean differences between high-performing and other leaders. Executive leaders were differentiated into high performers and others based upon total annual remuneration. This measure poses some problems (see Kerr & Bettis, 1987, for a recent discussion of this literature) though it is has been demonstrated (Lewellan & Huntsman, 1970; Murphy, 1985; Seligman, 1984; Smith et al., 1984) that chief executive pay is associated with higher firm performance. Therefore executive compensation was selected as the best available proxy for reflecting the impact executives have upon their organizations. The problem with the choice of compensation as a proxy for executive performance is that it could be confounded by other factors that have previously been demonstrated to be related to executive compensation. Among these factors are the employers’ ability to pay and the executive’s human capital assets (Agarwal, 1981; Gomez-Mejia, Tosi, & Hinkin, 1987; Selgiman, 1984). To reduce this confounding effect of other factors, compensation was regressed onto (1) the firm’s ability to pay and (2) human capital assets. The employer’s ability to pay is reflected in two measures; the relative size of the organization (measured by total assets), and the relative value of the firm (measured by the firm’s annual market value). The executive’s human capital assets were assessed by the executive’s experience or tenure (measured by the number of years as CEO) and whether the executive was an internal or external selection (where CEOs hired from outside the company were coded as “1” and those hired internally were coded as “0”). All these variables were obtained from Forbes. The resulting residual scores thus remove the variance attributed to these factors from the executive’s compensation and may be construed as a proxy measure of the premium that the board of directors is willing to pay the executives in order to reward them for being an effective leader.’ That is, high performing leaders were identified as those receiving the highest residual salaries (adjusted to 1985 dollars, see note 1) consistently across their careers during the 15 year period (i.e., 1971-1985). In this study, three distinct contrasts were conducted where high performing leaders are defined as those leaders receiving the top lo%, 20%, and 30% of residualized total remuneration. In these analyses, the high performers were dummy coded with a 1 and all other executives were coded 0. The standardized leader effect size (d,) was computed with the formula suggested by Landy et al. (1982, pg. 27):
where Mnpe= the mean performance of the high-performing executive; MO, = the mean performance of other executives; and SD = the pooled within-group standard deviation3 Three separate measures were selected as the financial measures of corporate performance: earnings per share, return on equity and net income. These measures were selected because they provide a relatively comprehensive picture of organizational success (Joy, 1977; Pringle & Harris, 1984).
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In summary, total remuneration, adjusted for the employer’s ability to pay and the executive’s human capital assets, was expected to reflect the leader’s competence, as perceived by the board of directors. High performing leaders are variously defined as those leaders scoring in the top lo%, 20$?&or 30% of the sampled executives. The effectsize (~4) parameter utilizes this distinction between high performers and other leaders to depict the standardized difference in job performance between the average person in the high performing group and the average person in the mediocre group across the three performance measures. For each performance variable, the average value for either group is based upon the sum of the annual performance values (adjusted to 1985 dollars, see note 1) during the executive’s tenure divided by the total tenure of those executives over the 15 year period (i.e., 1971-1985) assessed in this study. Tax Rate This parameter is based upon the average tax rate reported by the organizations the 15 year period as reported in Value Line Investment Surveys.
over
Cost Fstimates Although a search of the executive recruiting literature failed to reveal the necessary information, informal discussions with professional executive recruiters led to the conclusion that one-third of an executives’ first year cash salary is an effective estimate for these recruiting costs.4 Estimating SDy For the present study, two modifications of the Schmidt et al. (1979) procedure were necessary. First, SDy estimates were based upon a single position across multiple organizations, rather than within one organization. Nevertheless, financial analysts regularly make such comparisons across organizations, so it was expected that they could provide a useful estimate of the value of different levels of leader effectiveness. A recent study (Schmidt, Hunter, Outerbridge, & Trattner, 1986) used such a crossorganizational approach to generalize effects to the entire Federal government. Second, dollar estimates were not requested from the financial analysis because the executives were from organizations of considerably different sizes. These size differences make a comparison of dollar-based, performance-related estimates of executive influence uninterpretable. Instead, a percent estimate of the executive’s impact upon net income was requested because it is a metric that is not confounded by organizational size. These estimates were converted into dollar values for the sample as follows: The analysts’ mean SDy percent estimates was multiplied with the sample’s average net income, where net income data was obtained from Forbes. In addition to this mean SDy value, a variance estimate expressed in dollars, was obtained by multiplying the standard error of these SDy estimates with average net income. To obtain this estimate, the analysts were informed that the purpose of their ratings was to provide an estimate of the impact that manufacturing executives (from the Fortune 500) would have upon an organization’s total net income. Ratings were requested for three different levels of executive performance; at the 15th (i.e., low performers), 50th (i.e., average performers), and 85th (i.e., superior performers) percentile. After these different performance levels were described, the financial analysts
Executive
15
Leadership
were asked to provide a percent estimate for the various performance following manner:
levels in the
Based on my experience I estimate the impact of an average (low performing, superior performing) executive on the net income of a Fortune 500 industrial company at ~ percent per year. (Please estimate to the nearest one-tenth of a percentile).
These percentile estimates, combined with average net income, provided 15th, 50th, and 85th percentile performance-equivalent dollar estimates. When such estimates are normally distributed, one can average SDyl (50th-15th) and SDy2 (85th-50th) to determine an overall SDy estimate (Schmidt et al., 1979). RESULTS The effect-size estimates (dJ for each dependent variable for the three separate analyses of high-performing executive samples are reported in Table 1. As expected, the highperforming groups mean values increased as the analyses progressed from the top 30% of high-performing executives to the more exclusive top 10% sample across all three organizational measures. Nevertheless, the actual effect-size estimates generally did not vary much and tended to converge on effect-size values in the low 20s.’ Therefore, for purposes of discussion we will focus on the return on equity value reported for the top 20% of high-performing executives as a representative value across the three organizational measures. All other parameter estimates are reported in Table 2.
Table 1 Average Effect Size Estimates for Three High-Performing Samples of Executives Across Three Organizational Measures Variables
New Income Earnings Per Share Return on Equity
Hi- Perf Mean
Other Mean
d,
Standard Error
N’
(Top 10%)
(Other 90%) 426.61 3.454 .218
.198 ,352 ,223
.0771 .0762 .0779
190 190 182
4a4.475= 4.126 .246 (Top
20%)
Net Income Earnings Per Share Return on Equity
473.09 = 4.589 .240
410.907 3.44 .216
,193 ,307 ,219
.0567 .0562 .0571
392 392 380
Net Income Earnings per Share Return on Equity
(Top 30%) 501.659’ 4.145 .243
(Other 70%) 448.52 3.509 ,221
,163 ,157 ,172
.0490 .0486 .0496
600
Notes:
600 575
I N-sizes depict the number of years data was available for the top lo%, top 20%, or top 30% subsamples (Note, total N-size = 1,980). N-sizes within a particular subsample (e.g., top IO%), vary across the four organizational measures due to missing values. ’ Reported in thousands of dollars.
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Descriptive Variable
i T SDy6 TR c0
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Table 2 Statistics of Parameter Estimates
Mean (M)
Std. Error (s)
Size (n)
.lO” 10.344 $30,405,000 ,388 $171,311
NA so7 $5,088,000 .00309 $6,820
NA 220 41 2032 209
Notes: Where the variables are: i = cost of capital or discount rate, which in this study is assumed to remain constant; T = average tenure of executive leaders; SDy = dollar value of one performance standard deviation; TR = tax rates; and Co = cash outlays required to retain the intervention. ’ This value was selected as an “average” discount rate over the 15-year period of analysis (1971-1985). It should be noted that an increase of 5 percent (i.e., to .15) would result in a 19% reduction in the overall utility estimate. b SOY entries are based on the product of the financial analysts’judgments and net income (as reported in Forbes).
Table 3 Mean and 95% Confidence Interval Values for Three High-Performing Executives Across Three Organizational Measures Variables
Mean”
Upper Bout&
Samples of
Lower Boun&
Net Income Earnings Per Share Return on Equity
Top 10% of Executives vs. the other 90% $41,566 $22,959 $40,898 $63,116 $25,871 $44,964
$4,353 $18,680 $6,779
Net Income Earnings Per Share Return on equity
Top 20% of Executives vs. the other 80% $22,377 $40,892 $56,681 $35,656 $44,417 $25,405
$3,862 $14,632 $6,393
Net Income Earnings Per Share Return on Equity
Top 30% of Executives vs. the other 70% $18,882 $36,888 $18,183 $36,096 $38,081 $19,931
$876 270 $1,779
’ Reported in thousands
of dollars.
The SDy value reported in this study reflects the product of the financial analysts’ percent-impact estimates and the average net income of the organizational sample, as reported in Forbes. Utilizing the Schmidt et al. (1979) procedure for the financial analysts’judgments, we computed a value of 14.917% (std. error = 17.804%) for SDyl (i.e., 50-15) and 15.295% (std. error = 14.595% for SDy2 (i.e., 85-50). The average value between these estimates was 15.106% (std. error = 2.528%) and the average net income, based upon the sample from Forbes, was $201,278,000. Therefore, the overall mean value of one standard deviation in executive job performance, expressed in dollars, is the product of the terms or $30,405,000 (std. error = $5,088,000). Applying the utility equation (equation 1) to the various parameters’ mean values reported in Table 2 with the return on equity effect-size estimate for the top 20% of high performing executives (as reported in Table I), resulted in utility point estimate (U) of $25,405,417 over an executive’s average tenure (after accounting for taxes). This
17
Executive Leadership
suggests that an effective leader contributes a significant financial benefit to an average Fortune 500 manufacturing company. Finally, solving for equation 2 provides an assessment of the impact attributed to the uncertainty of these parameters. This analysis demonstrates that the 95% confidence interval around the utility point estimate ($6,393,468 L $25405,407 5 $44, 417, 365) exceeds the breakeven value, further illustrating the instrumentality of a high-performing executive. It should be noted that these results are based upon an effect size of .22. Nevertheless, effect size estimates from the other organizational measures generally resulted in comparable outcomes, with mean values ranging from 18 million to 41 million dollars as reported in Table 3. The lower confidence bound ranged from $270,096 to over $18 million, excluding zero in all cases, thus demonstrating the positive utility of a high-performing executive to an organization.
DISCUSSION This study estimates the value of high performing executives through the application of a linear decision-theoretic utility procedure. The results indicate that a highperforming executive has a substantial impact (in terms of dollars) on the economic performance of a Fortune 500 company. It is interesting to note that the average percentimpact estimates (SDy values) of the financial analysts (. 15 1) in this study are consistent with the average leadership effect sizes computed from archival sources by Lieberson and O’Connor (1972) and Salancik and Pfeffer (1977). Across all economic variables in their respective studies, Lieberson and O’Connor’s data show an average effect size of. 148 for administration (leadership) succession with an appropriate (3-year) time lag. Salancik and Pfeffer’s data yields an average effect size of .161 across all budget categories, when computed as proportions of the total city budget.6 Though it is true that the average effect-size from the executive leadership literature is based on a limited number of studies, many critiques regarding the relatively small effect of leadership on organizational performance have been made using these same studies and effectsizes (see Day & Lord, 1988, for a discussion of this issue). This consistency across the objective and subjective estimates of an executive leader’s effect on organizational performance has important implications for the notion that leadership is a heroic, over-used concept. The consistency between our results and those from the executive succession studies is contrary to the recent conclusions of Meindl and his associates (Meindl et al., 1985; Meindl & Ehrlich, 1987). Using samples of undergraduate and graduate students who were novice estimators of leader impact, they concluded that performance outcomes were over-attributed to leadership. The present study, using judgments of experts familiar with the extent of executive impact (i.e., financial analysts), found that the average estimate judgments correspond to independently derived, objective statistical results. This suggests that a heroic, romanticized explanatory concept may be used by individuals who are less sophisticated, novice judges of leadership effects. Expert/ Novice Differences
That experts and novices Research has demonstrated
should differ in their respective that expert/novice judgments
judgments is expected. often differ within a
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particular knowledge domain (e.g., Chi, Glaser, & Rees, 1982; Fredrickson, 1985; Larkin, McDermott, Simon, & Simon, 1980). There is a relatively straightforward explanation for the differences in results between our study and those of Meindl and associates (Meindl et al., 1985; Meindl & Ehrlich, 1987). Experts provide more accurate judgments-whether it is estimating the financial impact of high-performing executives or generating solutions to physics problems (Chi et al., 1982)-because they can rely on extensive, context-specific knowledge structures to process information (Galambos, Abelson, & Black, 1986). On the other hand, novices are more likely to use more general attributional procedures based on a leader’s general salience (Taylor & Fiske, 1978) since they lack detailed and organized, context-specific information. Therefore, despite attempts to reduce the salience level of a leader in written vignettes (Meindl et al., 1985, Study 6), it should come as no surprise that novices attribute a higher degree of control to leadership simply because they do not know the detailed contextual framework necessary for considering the effects of other factors. Thus financial analysts, experts in this domain, have probably observed numerous succession events of chief executives, and the extent of such successions’ impact on organizational performance. Therefore, the consistency between financial analysts’ SDy judgments and objectively-derived values suggests that financial analysts can be quite accurate and capable of incorporating this knowledge of succession effects. Executive leader Utility The results from this study suggest that a high-performing executive’s impact on firm performance would be at least 15 percent higher than the average-performing executive. The value of these performance differences is expected to exceed 25 million dollars (after taxes) for a Fortune 500 company. It should be noted that the computed dollar values are representative only of those large, manufacturing Fortune 500 organizations sampled in the present study, although such effects on corporate performance are relevant and crucial to smaller organizations. Moreover, because top-level leaders in small organizations have a generally large latitude of action (Hambrick & Finkelstein, 1987), it might be argued that this percent estimate is too low for these organizations because their executive leaders have a much greater effect on the performance of their organizations. The significance of the results from the present study are further underscored by the fact that it is entirely possible that high-performing executives would have no utility. For example, the financial analysts could have said they had little impact, giving us a SDy estimate around zero. In addition, and most convincing, there may have been no effect (i.e., no mean difference in organizational performance) between highly compensated and other executives. Although the first possibility could reflect perceptual bias, the second could not. In fact, as the results from a recent study suggest (Tosi & Gomez-Mejia, 1989), executive compensation would be expected to be a better indicator of leader performance in owner-controlled than management-controlled firms. Therefore, our effect size (d,) estimate would likely have been larger if we had been able to restrict our sample to owner-controlled firms. However, since our study presumably included both types of firms, the relationship between executive compensation and performance resulted in a weaker effect size estimate than would have occurred in a sample of owner-controlled firms.
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Nevertheless, there remains the issue of executive compensation and the possibility of reverse causality. That is, executives may be highly-compensated for good organizational performance, when in fact they had no impact on that performance. Although this interpretation cannot be dismissed, there are arguments that contradict it. First, in this study, executive pay was regressed onto firm performance (i.e., market value) prior to using executive compensation as a proxy for leader performance. Second, this reverse causality supposition conflicts with the estimates of executive impact provided by financial analysts who are familiar with succession events. Third, succession studies, which are not subject to such reverse causality effects, find that the amount of variance associated with succession is very similar to the SDy estimates of our expert financial analysts. In addition, succession studies have found greater effects for high as compared to average pe~o~ing leaders both when this distinction was based on salary (Smith et al., 1984) and when it was based on past performance (Pfeffer & DavisBlake, 1986). These alternative means of differentiating high versus average performing leaders produced very similar results. This issue actually is one facet of the larger debate over the causal relationship between leader behavior and organizational performance. Even though leadership does not explain a majority of the variance in organizational performance, it should not be expected to do so. Further, although the average effect size for leadership is relatively small (generally 20%-25%, with approximateiy 75%80% of the variance in performance being attributed to other factors), the financial impact of effective leadership is substantial-an average estimate of over 25 million dollars (after taxes) throughout an executive’s average career span. This is hardly trivial. Practical Implications
This last point underscores the important practical implications of the results of our study pertaining to two specific areas: (1) selection/succession of top-level executives, and (2) executive development. If the average impact of effective leadership of more than 25 million dollars over the average tenure for an executive is confirmed in future research, it argues that the considerable amount of time and money spent by organizations in recruiting and succession planning at the top executive levels is a sound investment. Our results also imply that developing or “grooming” individuals for top-level positions could help an organization maximize its leadership effectiveness and organizations pe~o~ance. This requires a cla~cation of the specific knowledge or abilities possessed by high-pe~orming executives. Although there has yet to be a systematic investigation identifying the means by which top-level executives influence organizational performance, there is some work which provides some direction concerning methodological considerations and types of executive actions that may be most influential (e.g., Day & Lord, 1988; Hambrick & Finkelstein, 1987; Tushman & Romanelli, 1985). NOTES 1. To ensure that all variables were measured in dollars of equal value across the various years (19’71-1985),all dollar values were adjusted to 1985 dollars based upon the Consumer Price Index as reported in ~o~r~~y Labor Review.
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2. We thank one of the reviewers for the suggestion that the compensation residual, after controlling for other variables that affect compensation, could represent the premium the CEO receives because he/she has been judged to be competent by the board of directors. That is, we assume that the directors have attempted to control executive behavior by making compensation and termination decisons based on their perceptions of firm performance. 3. In this study, the standard error of the true effect size (S) was estimated by applying the large-sample approximation suggested by Hedges (1982). 4. Other cost considerations include variable compensation costs that are based on organizational performance. While those variable costs could impact on the utility estimate, they do not substantially affect the outcome of the present study, Therefore, variable costs are not included in order to keep from complicating the issue any further. 5. The effect sizes (d,) were also calculated using year to year changes in the performance variables (available upon request from the first author). The resulting effect-size estimates were roughly equivalent to those based on overall averages (see Table 1). 6. These figures are available in the original sources or can be found in Day and Lord (1988, Table 1).
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