Read EJEFAS_28_13.pdf text version

European Journal of Economics, Finance and Administrative Sciences ISSN 1450-2275 Issue 28 (2011) © EuroJournals, Inc. 2011 http://www.eurojournals.com

Executive Compensation and Firm Performance: An Empirical Examination

Eddy Junarsin Faculty of Economics and Business, Universitas Gadjah Mada E-mail: [email protected] Abstract This paper purports to see the relationship between performance and compensation for the period of 1992-2008. Performance is proxied by four measures: (1) return on assets (ROA); (2) market-to-book ratio (MB); (3) earnings per share (EPS); and (4) return. Meanwhile, compensation is analyzed using five types of pay: (1) total compensation, (2) salary, (3) bonus, (4) restricted stock grant, and (5) options granted calculated with Black-Scholes value. Data utilized are annual data from CRSP, Compustat, and Execucomp databases. This study finds that total compensation is positively related to ROA, MB, EPS, and return. In addition, the coefficients on the performance variables are stronger for highest paid executives. The proportion of salary to total compensation is negatively and significantly related to ROA, MB, EPS, and return, implying that the better the performance of a company the less important will be its executive compensation in the form of basic salary. The relationships between the proportion of bonus to total compensation and ROA, EPS, and return are positive and significant while MB is negatively associated with the proportion of bonus to total compensation. Subsequently, the proportion of restricted stock grants to total compensation is found to be positively related to EPS and return. MB is not significantly related to the proportion of restricted stock grants to total compensation while the coefficient on ROA is significantly negative only for the case of Executive 1. Meanwhile, the proportion of option grants value to total compensation is positively related to ROA and MB, but has negative relations with EPS and RET. It is also found that the relationships between total compensation and lagged values of MB, EPS, and return are positive. However, coefficient on ROA is not significant. Furthermore, the coefficients on MB, EPS, and return are stronger for higher level executives. However, the associations between the change in total compensation and the changes in ROA, MB, EPS, and return are not significant. Total compensation relative to industry category compensation has significantly positive relationships with ROA, MB, and EPS relative to their industry category ROA, MB, and EPS. However, the coefficient on RETIN is only significant for the case of Executive 3. Subsequently, the coefficients on ROA, MB, EPS, and return are stronger for firms in higher size rank (rank 3) than in lower size rank (rank 1). Two exceptions here are: (1) MB for Executive 1 shows a descending pattern from size rank 1 to size rank 3 and (2) coefficients on EPS*DRank1 are insignificant. Eventually, findings suggest that return is negatively and significantly affected by lagged value of option grants (valued using Black-Scholes method).

Keywords: Executive compensation, return on assets, market-to-book ratio, earnings per share, return, total compensation, salary, bonus, restricted stock grant, and options.

164

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011) JEL Classifications Codes: G30, G34

1. Introduction

This study purports to see the relationship between performance and compensation for the period of 1992-2008. Performance is proxied by four measures: (1) return on assets (ROA); (2) market-to-book ratio (MB); (3) earnings per share (EPS); and (4) return. Meanwhile, compensation is analyzed using five types of pay: (1) total compensation, (2) salary, (3) bonus, (4) restricted stock grant, and (5) options granted calculated with Black-Scholes value. Data utilized are annual data from CRSP, Compustat, and Execucomp databases. Executive compensation has been a hot and debated topic in corporate finance for the last two decades. Academics and practitioners interested in corporate governance see the executive compensation as one of the mechanisms to manage agency conflicts in corporate life. However, this remedy yields two possible consequences. If used appropriately, executive compensation can bond managers to owners so as to enhance shareholder wealth. On the other hand, the misuse or dysfunction of this mechanism will impoverish managerial entrenchment and moral hazard. Previous and recent financial crisis have kindled people's interests in this area since they consider the executive compensation to be one of the salient factors triggering corporate scandals. Previous empirical findings mostly find that executive compensation is influenced by firm size and performance. Nevertheless, the recent increase in executive compensation has far exceeded the improvement in size and performance. Furthermore, the higher the wealth of the CEO, the higher the incentives that he or she is willing to acquire. Meanwhile, for director compensation, the pay has also increased substantially for the last decade. It seems that companies always adjust to the market level of director compensation. However, these adjustments are not symmetric; upward adjustment tends to be quicker than does downward adjustment. When equity value increases, there is no immediate offset to the director compensation. This research is aimed at observing the relationship between performance and compensation for the period of 1992-2008. Performance is proxied by four measures: (1) return on assets (ROA); (2) market-to-book ratio (MB); (3) earnings per share (EPS); and (4) return. Meanwhile, compensation is analyzed using five types of pay: (1) total compensation, (2) salary, (3) bonus, (4) restricted stock grant, and (5) options granted calculated with Black-Scholes value. Data utilized are annual data from CRSP, Compustat, and Execucomp databases. This study finds that total compensation is positively related to ROA, MB, EPS, and return. In addition, the coefficients on the performance variables are stronger for highest paid executives. The proportion of salary to total compensation is negatively and significantly related to ROA, MB, EPS, and return. It implies that the better the performance of a company, the less important will be its executive compensation in the form of basic salary. Model 3 provide evidence that the relationships between the proportion of bonus to total compensation and ROA, EPS, and return are positive and significant while MB is negatively associated with the proportion of bonus to total compensation. Subsequently, the proportion of restricted stock grants to total compensation is found to be positively related to EPS and return. MB is not significantly related to the proportion of restricted stock grants to total compensation while the coefficient on ROA is significantly negative only for the case of Executive 1. Meanwhile, the proportion of option grants value to total compensation is positively related to ROA and MB, but has negative relations with EPS and RET. It is also found that the relationships between total compensation and lagged values of MB, EPS, and return are positive. However, coefficient on ROA is not significant. Furthermore, the coefficients on MB, EPS, and return are stronger for higher level executives. However, I do not find significant associations between the change in total compensation and the changes in ROA, MB, EPS, and return. Table 18 reports that total compensation relative to industry category compensation has significantly positive relationships with ROA, MB, and EPS relative to their industry category ROA, MB, and EPS. However, the coefficient on RETIN is only significant for the case of Executive 3. Subsequently, it is shown in Table 19 that the coefficients on ROA, MB, EPS, and return are stronger

165

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

for firms in higher size rank (rank 3) than in lower size rank (rank 1). Two exceptions here are: (1) MB for Executive 1 shows a descending pattern from size rank 1 to size rank 3 and (2) coefficients on EPS*DRank1 are insignificant. Eventually, findings suggest that return is negatively and significantly affected by lagged value of option grants (valued using Black-Scholes method). This paper is organized as follows. Section 2 reviews previous literature. Data and methods are discussed in Section 3. Subsequently, Section 4 provides empirical findings. Eventually, Section 5 concludes.

2. Literature Review

Bebchuk and Fried (2004) wrote a discussion paper that tries to relate executive pay to performance. The mainstream for academics' study of executive compensation assumes that compensation is the product of arm's-length contracting, which states that managers and directors only have a professional relationship by doing their best jobs and interests. The arm's-length contracting view has led researchers to assume that executive compensation arrangements will tend to increase value. However, this paper indicates that managerial power has played a key role in determining the executive compensation. The authors explain that U.S. corporate governance system gives the board substantial power, and counts on them to monitor and supervise the company's managers. Many experts think that flawed compensation practices have been widespread, persistent, and systemic, stemming from structural defects in the underlying governance structure that enable executives to exert considerable influence over their boards. Hence, the problem is with the system of arrangements and incentives within which directors and executive operate, not with the moral virtue or competencies of directors and executives. Directors have various economic incentives to support arrangements favorable to the company's top executives on account of several factors: (1) incentives to be re-elected, (2) CEOs power to benefit directors, (3) friendship and loyalty, (4) collegiality and authority, (5) cognitive dissonance and solidarity, (6) the small costs of favoring executives, (7) ratcheting, (8) limits of market forces, (9) new CEOs, and (10) firing of executives. This bias may create a situation where pay is not tied to performance anymore, especially after the invention and availability of innovative compensation methods such as equity or options compensation. These researchers suggest that three areas of improvements are required in order to achieve a sound compensation practice: (1) improving transparency, (2) improving pay arrangements, and (3) improving board compatibility and independence. Subsequently, Bebchuk and Grinstein (2005) explore this topic further by examining the extent to which pay growth can be explained by changes in firm size, performance, and industry mix. They use sample taken from the standard Execucomp database for the period of 1993-2003. Executive's total compensation is defined as the sum of the executive's salary, bonuses, long-term incentive plans, and the grant-date value of restricted stock awards and grant-date Black-Scholes value of granted options. They document that among S&P 500 firms, average CEO compensation soared from USD3.7 million in 1993 to USD9.1 million in 2003, which shows an increase of 146%, while the average compensation of top-five executives increased from USD9.5 million in 1993 to USD21.4 million in 2003, booking an increase of 125%. These increases do not matter as long as the improvement in shareholder wealth corresponds with executive pay increase. In the researchers' observation, S&P 500 firm size increased substantially between 1993 and 2003, indicating an increase of 40% from 1993-1995 to 2001-2003. They then regress log compensation variable on log sales that represents size, log ROA that represents profitability, log returns, and year dummies (1994-2003) with fixed effects to analyze whether compensation level changes during the examined period after controlling for changes in size, profitability, and returns. The results show that compensation level increases far beyond what can be attributed to changes in size and performance. The year dummy variables increase monotonically until 2000, then decline afterwards but are still higher than those in 1999 and previous years. Furthermore, it is evidenced that changes in firm size and performance can only explain 40% of the compensation

166

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

increase, meaning that 60% of the compensation increase remain unexplained by changes in size and performance. Another regression with the same dependent and independent variables is conducted by involving two dummies: (1) new economy and (2) new industry. Controlling these additional dummies, they find that CEO compensation level in 2003 exceeds by 115% the level predicted by the regression whereas top-five executive compensation level exceeds the predicted level by 79%. Finally, the authors conduct regressions by differentiating between equity-based compensation and cash compensation. The findings show that the proportion of equity-based compensation in total compensation increases from 37% in 1993 to 55% in 2003. Surprisingly, this increase in equity-based compensation is not followed by a decline in cash compensation; rather, the cash compensation also increases by 40% between 1993 and 2003. Meanwhile, using companies listed on the A-list of the Stockholm Stock Exchange from 1993 to 1999, Becker (2006) analyzes Swedish data to look at the relationship between CEO wealth and incentive strength. His final sample consists of 80 companies and 7 years although the data are imbalanced. It is hypothesized that a wealthy CEO is less risk averse than a less wealthy one, leading to a conjecture that wealth could be a proxy for risk aversion. This is the main idea of his research since agency theory postulates that there should be a positive association between risk aversion and incentives. A wealthier CEO, if less risk averse, will have to be given higher pay to induce a desired action. In this research, the author utilizes CEO's nonfirm wealth (the value of share and option holdings in the CEO's own firm subtracted from total wealth). In order to observe only nonfirm wealth, 0.7 multiplied by value of shares are deducted from the wealth calculation. Due to the ambiguity with respect to the appropriate measure of incentive strength, two methods are introduced: (1) money at stake, defined as the sum of stock owned by the CEO multiplied by share price and the number of options held times the estimated delta times the share price; and (2) share of the company, computed as number of shares plus number of options multiplied by delta, divided by the total number of shares outstanding. The researcher later shows that findings derived from both alternatives are similar. The median share of the company ranges from 0.03% to 0.05% over the sample period while option holdings vary from 44% in 1993 to 71% in 1999. Other variables employed are volatility, firm size, CEO age and tenure, and large owner dummy. The latter variable is taken to indicate better governance and monitoring of the CEO. Large owner means that he or she controls at least 5% of votes, and this definition excludes ownership by institutions, government, and management. The researcher then regress incentive strength (using both money at stake and share of company) on independent variables such as log sales, log market value of equity, volatility, age, tenure, log wealth, negative wealth dummy, and times dummies. Findings indicate that age is negatively related to incentives, tenure is positively related to incentives, while the other control variables show no significance. More importantly, wealth has a positively significant relationship with incentives. High wealth CEOs have stronger incentives in both measures. Two endogeneity factors may appear and bias these results: (1) the positive relationship between incentives and wealth is influenced by CEO power or (2) the positive relationship is moderated by CEO skills. At the last section of this research, these two factors are dealt with, and the overall conclusion does not change. With respect to corporate governance, this study includes in the regression the large owner dummy as explained above. However, while wealth remains significant, the interaction between wealth and the large owner dummy is not significantly related to incentives, meaning that more poorly governed firms do not exhibit a stronger relationship between wealth and incentives. In another setting, Farrell, Friesen, and Hersch (2008) conduct unique and slightly different research, which sees compensation in the perspective of directors instead of executives. Unlike executive compensation, director compensation is designed for a group of individuals where the directors may receive an annual retainer, meeting fees, committee fees, and equity awards. Equity compensation for directors can be awarded in the form of fixed-value grants or fixed-number grants. Sample in this study is taken from 237 Fortune 500 firms for the period of 1998-2004. In this period, they observe that total director compensation increases by 45%. As shown in the descriptive section of Table 1 below, there is a tendency to rely more on fixed-value equity compensation. This trend

167

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

suggests that the equity component of director compensation is becoming more transparent. The researchers subsequently conduct a regression of log total director compensation on log sales, marketto-book ratio, debt ratio, ROA, and one-digit SEC industry dummies. The general finding shows that size (represented by sales) and growth opportunities (proxied by market-to-book ratio) exert positively significant effects on the total director compensation. The authors continue to analyze adjustments to director compensation. Academics and practitioners have a conjecture that director compensation may change in response to changes in firm size, fluctuation in market salary for directors, or volatility in equity market (especially for fixed-number equity compensation). Hence, the authors regress changes in total compensation on deviation of the compensation from its predicted market level of compensation. The predicted level of compensation is generated from the previous regression. If firms in the sample set a target level of director compensation, the coefficient on the Deviation will be negative, suggesting that the firms adjust the compensation toward its predicted value. The results indeed appear to substantiate the hypothesis. The coefficient on Deviation variable is negative, and they find that the average firm takes at least four years to fully adjust to its market level. However, these adjustments are not symmetric; upward adjustment tends to be quicker than does downward adjustment. When equity value increases, there is no immediate offset to the director compensation.

Table 1: Summary of Research on Compensation

Bebchuk and Grinstein Year of publication Research period Observed country Sample Average CEO compensation Average director compensation Proportion of equity compensation Fixed-value equity compensation Fixed-number equity compensation Dependent variable Log CEO compensation CEO incentives Becker 2006 1993-1999 Sweden 80 A-list companies listed on the Stockholm Stock Exchange SEK3.4 million Farrell et al. 2008 1998-2004 U.S. 237 Fortune 500 firms

2005

1993-2003 U.S. S&P 500 companies listed on the Execucomp USD7 million

USD122.4 thousand

50% 55% 93% (of total director compensation) 36% to 57% 70% to 63% Director compensation +***

Log sales +*** Log ROA +* Log returns +*** Year dummies +*** Log market value of equity Independent Volatility Age variable Tenure Log wealth Log wealth * large owner dummy Negative wealth dummy *significant at 10% level, **significant at 5% level, ***significant at 1% level

-** +* +* + +*

+**

Subsequently, the article written by Rajgopal et al. (2006) brings about an interest in market indexing of compensation, also known as relative performance evaluation (RPE). This research harnesses S&P 500 firms and yields 2,343 CEO-firm-year observations from 1993 to 2001. Agency theory predicts that the market-wide component of a firm's returns should be removed from

168

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

compensation package since this is not achieved by executives' managerial capabilities and efforts. Nowadays, executive compensation contains a large fraction of equity and options such that an increase in general equity market value will automatically boost up the executive pay. Ironically, executives have little influence on the equity market; the fluctuation of the equity market is beyond the control of executives. Rajgopal et al. (2006) differ from the strand of Bebchuk et al. regarding the view on executive and board relationship. Bebchuk et al. support the perspective that managers are rent seeking; they argue that paying managers for market-wide increases in stock prices is inconsistent with the arm's-length relationship framework. On the contrary, Rajgopal et al. (2006) ­ although they do not agree with the optimal contracting view (arm's-length relationship) ­ do not support the rent-seeking (managerial influence) perspective. Rather, they see that firms optimally reward CEOs for riding a bull market if their pays vary with the economy's fortunes. Since CEO talent is scarce, demand for talented CEOs increases as the economy booms such that firms must pay CEOs more to retain them. Accordingly, increasing pay with rising market levels potentially enables firms to retain talented executives. The main feature of their study is the inclusion of talent in compensation analysis. Since talent is difficult to measure, two proxies for CEO talent are used: (1) the number of articles in the major U.S. and global business newspapers and wire services in which the CEO's name appears and (2) past industry-adjusted return on assets (ROA). It is believed that more talented CEOs are likely to be cited and recognized by the business press for outstanding ideas or business models. Besides, talented CEOs are more likely to produce superior industry-adjusted ROA performance. The propensity of firms to let CEO compensation "ride the bull market" should increase with these proxies for CEO talent, arguing that more talented CEOs should face less RPE. Indeed, it is found that most compensation committees do not practice RPE; they do not earnestly filter out market-wide performance from the CEO compensation comprising equity- and cash-based components. In the empirical examination, the researchers regress the change in total CEO compensation on the change in shareholder wealth in firm, the change in shareholder wealth in industry, the change in shareholder wealth in industry * cumulative distribution function (cdf) of articles published citing the CEO, the change in shareholder wealth in industry * ROA, the change in shareholder wealth in industry * cdf of CEO tenure, the change in shareholder wealth in firm * cdf of CEO age, the change in shareholder wealth in firm * cdf of size, the change in shareholder wealth in firm * cdf of variance of shareholder wealth, with two-digit industry dummies and year dummies. In general, the findings suggest that the sensitivity of CEO compensation to market-wide performance is systematically higher for CEOs who enjoy greater press coverage and superior industry-adjusted ROA during the previous three years. These results are in line with the view that market-wide shocks increase demand for CEO talent outside the firm, which in turn forces some firms to increase compensation levels to retain their talented CEOs. Moreover, they also examine whether CEO compensation is allowed to float with the market index only when the market is up but not when the market is down. They find evidence that CEO compensation is basically shielded from a market downturn in most specifications, but the results related to talented CEOs are mixed depending on the proxy for talent and the specific index chosen (industry index or market index). Overall, this research reinforces the findings of other research on compensation with respect to the fact that executive compensation is influenced by a myriad of elements beyond firm size and performance. It is also hard to decide whether arm's-length relationship or rent-seeking view can better explain the mechanisms of executive compensation and board monitoring. Subsequently, firms experiencing market-wide shocks have to increase compensation levels to retain their talented CEOs, possibly leading to a temporary deviation from their equilibrium levels. One common conclusion found in this research relative to the other research's finding is that upward adjustment in executive compensation is much easier and faster to be executed than the downward adjustment.

169

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

3. Data and Methods

3.1. CRSP and Compustat Data From CRSP database, I collected PERMNO, CUSIP number, ticker symbol, year, company name, security return, adjusted shares outstanding, and end-of-previous-period capitalization data. On the other hand, data gathered from Compustat database included global vantage key (GVKEY), CUSIP number, ticker symbol, year, company name, total assets (TA), total equity (TE), operating income before depreciation (EBITDA), and net income (NI). Using Greene's (2010) SAS codes, I transpose these variables so as to get tidy observations for each firm in each year of all aforementioned variables. I merge these two databases on the basis of eight-digit CUSIP number, with observations with spelling distance of company name more than 75 being deleted. Secondly, observations that do not have perfect matches in the first-phase matching are then matched in the second-phase process based on ticker symbol, and again observations with spelling distance of company name more than 75 are deleted. This latter data set is subsequently merged into the perfect-match data set. Further data cleaning is conducted as follows. I delete observations with EBITDA less than -93 or greater than 15000, NI less than -600 or greater than 10000, TE less than -60 or greater than 50000, TA less than 3 or greater than 200000, capitalization less than 2000 or greater than 100000000, shares outstanding less than 450 or greater than 3000000. I also delete an observation if its GVKEY is missing. In order to synchronize the level of numbering between CRSP and Compustat, I multiply TA, TE, EBITDA, and NI with 1,000. This is due to the fact that CRSP data are stated in thousand unit whereas original Compustat in million unit. I find out that Execucomp data are also stated in thousand unit. The whole process in merging CRSP with Compustat data yield 8,974 firms and 45,247 firmyears.

Table 2: CRSP/Compustat Data by Year before Merging with Execucomp Data

Years 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Firms 4447 4085 4349 4135 4178 3828 4093 4180 4242 4190 3520

3.2. Execucomp Data I gather GVKEY, year, executive ID, executive full name, company name, SIC code, executive title, executive rank, total compensation, salary, bonus, restricted stock grant, and options granted calculated with Black-Scholes value data from Execucomp database. As measures of performance, I employ: (1) total compensation, (2) salary, (3) bonus, (4) restricted stock grant, and (5) options granted calculated with Black-Scholes value. These proxies were also used by Benson and Davidson (2008) to analyze pay-performance sensitivity. Total compensation comprises salary, bonus, other annual, restricted stock grants, long-term incentive payouts, all other, and value of option grants. In this paper, I only include three highest paid executives in each company. Subsequently, I clean the data by deleting observations with total compensation less than 110 or greater than 40000, options granted greater than 25000, restricted stock grants less than 0 or greater than 15000, salary less than 30, and bonus less than 0 or greater than 15000. As GVKEY, year, and SIC code are written in character code rather than numerical type, I harness Greene's (2010) SAS codes to convert them into numerical values. The final Execucomp data to be used in this study

170

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

encompass 2,836 firms and 65,060 firm-executive-years (comprised of three highest paid executives in each firm).

Table 3: Number of Executives by Year of Execucomp Data before Merging

Year 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Executive 1 373 1158 1538 1585 1630 1644 1696 1748 1716 1618 1637 1707 1712 1701 282 Executive 2 306 1130 1539 1588 1630 1650 1706 1775 1749 1634 1649 1719 1730 1728 282 Executive 3 72 1111 1524 1579 1621 1646 1694 1782 1757 1639 1638 1714 1726 1715 282

Executive 1 is the highest paid executive, Executive 2 the second highest paid executive, and Executive 3 the third highest paid executive. 3.3. Merged Data I merge CRSP/Compustat data with Execucomp data by GVKEY, producing 2,096 firms and 31,012 firm-executive-years.

Table 4: Comparison between Data before and Those after Merging

CRSP/Compustat 8,974 45,247 Execucomp 2,836 65,060 Merged Data 2,096 31,012

Firm Firm-executive-years

Table 5:

Number of Executives by Year after Merging

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 Executive 1 1150 1155 1229 1188 1258 1307 1380 1377 248 Executive 2 1152 1165 1241 1191 1265 1313 1392 1399 248 Executive 3 1144 1166 1248 1194 1257 1314 1392 1392 247

Executive 1 is the highest paid executive, Executive 2 the second highest paid executive, and Executive 3 the third highest paid executive. Afterwards, I define the following variables: E B I T D A it (1) ROA =

it

T A it

M B it =

C A Pi t T E it

(2) (3)

E P S it =

N I it O S it

where:

171

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

ROAit = return on assets of firm i in year t, EBITDAit= operating income before depreciation of firm i in year t, TAit = total assets of firm i in year t, MBit = market-to-book ratio of firm i in year t, CAPit = capitalization of firm i in year t, TEit = total equity of firm i in year t, EPSit = earnings per share of firm i in year t, NIit = net income of firm i in year t, OSit = outstanding shares of firm i in year t. ROA, MB, EPS, and return are chosen to represent performance in order to make a balanced use of book and market values. ROA is a pure book value measure, return is a market value measures, whereas MB and EPS are derived from the combination between book and market values. An observations is considered a missing value if ROA is less than equal -1, MB is less than equal 0 or greater than 100, and EPS is less than equal -5 or greater than 100. I categorize SIC based on two-digit SIC code as follows:

Table 6: Categorization of SIC Code (U.S. Department of Labor 2010)

Category Agriculture, Forestry, and Fishing Mining Construction Manufacturing Transportation, Communications, Electric, Gas, and Sanitary Services Wholesale Trade Retail Trade Finance, Insurance, and Real Estate Services Public Administration

Two-Digit SIC Code 01 to 09 10 to 14 15 to 19 20 to 39 40 to 49 50 to 51 52 to 59 60 to 69 70 to 90 91 to 99

Predicated on these industry categories, I calculate the means of ROA, MB, EPS, return, total compensation, salary, bonus, restricted stock grant, and options granted calculated with Black-Scholes value for each category in each year. I also create three portfolio ranks for each year based on capitalization in order to observe size effect.

4. Findings

4.1. Descriptive Statistics The following tables and figures show the descriptive statistics for each type of executive.

Table 7:

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006

Variable Means of Data of Highest Paid Executives by Year

ROA 0.141 0.143 0.139 0.119 0.115 0.121 0.130 0.130 0.137 MB 3.336 3.142 3.290 3.288 3.176 2.285 2.944 3.089 2.888 EPS 1.291 1.389 1.376 0.801 0.871 1.206 1.596 1.703 1.499 Return 0.076 0.092 0.144 0.119 (0.119) 0.441 0.196 0.095 0.148 Salary 576.354 594.865 601.289 623.907 649.113 677.458 692.781 710.204 692.122 Bonus 553.740 642.872 713.869 585.031 676.014 788.197 933.339 1,016.557 966.780 Restricted Stocks 202.406 195.705 281.179 285.006 368.468 461.386 635.539 774.682 697.164 Option Grants 1,471.269 1,686.362 1,982.528 2,332.235 1,875.141 1,523.416 1,683.305 1,466.684 1,106.025 Total Compensation 3,107.830 3,446.459 3,915.390 4,100.718 3,891.992 3,856.008 4,358.848 4,501.941 3,837.016

172

Table 8:

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

Variable Means of Data of Second Highest Paid Executives by Year

ROA 0.141 0.143 0.139 0.121 0.117 0.121 0.130 0.131 0.136 MB 3.335 3.169 3.325 3.300 3.194 2.285 2.942 3.075 2.881 EPS 1.287 1.420 1.450 0.813 0.923 1.209 1.596 1.740 1.516 Return 0.080 0.093 0.139 0.122 (0.117) 0.443 0.198 0.099 0.152 Salary 387.648 397.701 396.702 406.136 418.194 435.688 442.069 449.626 447.956 Bonus 325.947 365.656 394.631 301.535 361.840 416.547 478.492 541.252 511.280 Restricted Stocks 121.089 159.304 133.381 158.454 176.002 256.137 280.890 357.299 409.158 Option Grants 801.545 1,104.910 1,302.257 1,348.687 1,064.333 839.548 794.144 727.352 554.836 Total Compensation 1,816.538 2,246.675 2,441.353 2,405.224 2,190.822 2,127.448 2,198.545 2,321.005 2,217.898

Table 9:

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006

Variable Means of Data of Third Highest Paid Executives by Year

ROA 0.141 0.144 0.139 0.122 0.117 0.121 0.130 0.131 0.139 MB 3.364 3.181 3.360 3.312 3.177 2.290 2.944 3.065 2.927 EPS 1.294 1.426 1.437 0.835 0.925 1.209 1.613 1.750 1.530 Return 0.079 0.098 0.144 0.120 (0.116) 0.447 0.199 0.099 0.152 Salary 305.904 320.280 320.435 336.081 343.357 361.273 370.930 378.601 389.083 Bonus 220.979 245.370 258.097 213.955 248.609 282.238 333.695 364.816 338.653 Restricted Stocks 84.939 114.237 105.794 102.043 133.358 154.447 219.002 268.051 369.852 Option Grants 555.235 776.846 890.829 928.011 675.297 576.408 641.526 543.092 502.379 Total Compensation 1,316.274 1,598.838 1,717.179 1,691.623 1,515.372 1,504.599 1,721.758 1,751.609 1,774.452

Figure 1: Differences in the Proportion of Salary to Total Compensation among Three Types of Executives

Figure 2: Differences in the Proportion of Bonus to Total Compensation among Three Types of Executives

173

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

Figure 3: Differences in the Proportion of Restricted Stock Grants to Total Compensation among Three Types of Executives

Figure 4: Differences in the Proportion of Option Grants to Total Compensation among Three Types of Executives

Figure 1 depicts that the third highest executives earn higher proportion of salary to total compensation than do the highest and the second highest paid executives. Meanwhile, the highest paid executives dominate the others with respect to the proportions of bonus, restricted stocks, and options grants to total compensation, as shown in Figures 2, 3, and 4. The second and the third highest paid executives do not show obvious differences in proportions of restricted stocks and option grants to total compensation.

Table 10: Comparison among Variables based on Executive Types and Size Ranks

Executive 1 Rank 1 2 3 1 2 3 1 2 3 ROA 0.104 0.141 0.144 0.106 0.141 0.144 0.106 0.141 0.145 MB 2.177 2.901 4.075 2.183 2.914 4.082 2.191 2.917 4.097 EPS 0.732 1.290 1.856 0.746 1.349 1.860 1.367 1.864 Return 0.183 0.117 0.102 0.184 0.117 0.105 0.186 0.119 0.108 Salary 473.662 599.438 861.595 308.362 383.242 565.030 255.111 317.773 461.589 Bonus 352.070 634.035 1,275.519 190.131 329.042 697.601 122.873 226.831 477.731 Restricted Stocks 149.487 339.410 771.952 66.541 153.799 425.080 44.411 104.858 321.315 Option Grants 619.488 1,364.692 3,207.592 325.388 735.116 1,874.143 235.904 529.972 1,302.885 Total Compensation 1,734.667 3,209.612 6,812.937 958.982 1,757.198 3,946.701 709.712 1,282.856 2,842.589

3

Table 10 indicates that the larger the size of a firm, the higher is its executive compensation in all types of pays (salary, bonus, restricted stocks, and option grants). This holds for all types of

174

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

executives. Most of performance variables follow the same pattern, except return that exhibits an inverse relationship with size rank. 4.2. Regression Analysis All regressions are conducted on three types of executives. The first model employed is: TCit = i + 1i ROAit + 2i EPSit + 3i MBit + 4i RETit + i TCit = total compensation of firm i in year t, ROAit = return on assets of firm i in year t, EPSit = earnings per share of firm i in year t, MBit = market-to-book ratio of firm i in year t, RETit = return on firm i in year t.

Table 11: Regression Results of Model 1 (Dependent Variable: TCit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 3056.560317 7.3966E-248 1607.039151 2.5006E-164 1211.096206 1.3115E-176 ROA 1650.292897 0.002698416 587.6051499 0.102335008 453.4877556 0.081466157 MB 170.3868762 3.57044E-32 129.7299723 1.11532E-43 87.35372907 4.99276E-38 EPS 171.6546524 2.64314E-21 123.5843404 1.15492E-30 78.40047264 3.27532E-24 RET 614.0521124 2.28876E-07 375.3444049 1.33565E-06 223.5242847 6.50214E-05

(4)

Results for Model 1 suggest that total compensation is positively related to ROA, MB, EPS, and return. In addition, the coefficients on the performance variables are stronger for highest paid executives. The following is my second model: SalaryPit = i + 1i ROAit + 2i EPSit + 3i MBit + 4i RETit + i (5) SalaryPit = the proportion of salary to total compensation of firm i in year t, ROAit = return on assets of firm i in year t, EPSit = earnings per share of firm i in year t, MBit = market-to-book ratio of firm i in year t, RETit = return on firm i in year t.

Table 12: Regression Results of Model 2 (Dependent Variable: SalaryPit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.364376629 0.00 0.40553339 0.00 0.435323508 0.00 ROA -0.19288671 7.76717E-13 -0.22245159 6.83964E-17 -0.26831499 1.00367E-24 MB -0.00719137 1.83059E-24 -0.00712718 5.22473E-25 -0.00804713 1.60903E-32 EPS -0.00702638 1.97481E-15 -0.0062316 3.53872E-15 -0.00620602 9.17495E-16 RET -0.04320786 9.86402E-14 -0.04659585 5.1108E-16 -0.04849484 5.5804E-18

Table 12 shows that the proportion of salary to total compensation is negatively and significantly related to ROA, MB, EPS, and return. It implies that the better the performance of a company, the less important will be its executive compensation in the form of basic salary. Subsequently, my third model is formulated as follows; BonusPit = i + 1i ROAit + 2i EPSit + 3i MBit + 4i RETit + i (6) BonusPit = the proportion of bonus to total compensation of firm i in year t, ROAit = return on assets of firm i in year t, EPSit = earnings per share of firm i in year t, MBit = market-to-book ratio of firm i in year t, RETit = return on firm i in year t.

175

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

Table 13: Regression Results of Model 3 (Dependent Variable: BonusPit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.173253749 0.00 0.169902112 0.00 0.156364229 0.00 ROA 0.207750048 1.22178E-21 0.175168967 2.20282E-18 0.183584514 4.35299E-23 MB -0.00233226 3.86047E-05 -0.00237931 4.22524E-06 -0.00234435 1.0077E-06 EPS 0.006319893 8.48834E-19 0.0051613 4.3395E-18 0.004708287 9.00145E-18 RET 0.066196093 5.47957E-45 0.060482766 3.20947E-44 0.061267261 7.67054E-53

Results for Model 3 provide evidence that the relationships between the proportion of bonus to total compensation and ROA, EPS, and return are positive and significant while MB is negatively associated with the proportion of bonus to total compensation. Model 4 is formulated as follows: RSGPit = i + 1i ROAit + 2i EPSit + 3i MBit + 4i RETit + i (7) RSGPit = the proportion of restricted stock grants to total compensation of firm i in year t, ROAit = return on assets of firm i in year t, EPSit = earnings per share of firm i in year t, MBit = market-to-book ratio of firm i in year t, RETit = return on firm i in year t.

Table 14: Regression Results of Model 4 (Dependent Variable: RSGPit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.073969674 3.3165E-126 0.069783731 2.1891E-132 0.065725211 1.2464E-122 ROA -0.04290777 0.024406381 -0.028738411 0.10071503 -0.020766459 0.225332578 MB 0.000485348 0.329793226 -0.000378215 0.403698217 0.000162967 0.71333145 EPS 0.003404118 5.58385E-08 0.002349236 6.44806E-06 0.002718304 8.2795E-08 RET 0.013241845 0.001278795 0.010684714 0.004676193 0.011067877 0.002648586

The proportion of restricted stock grants to total compensation is found to be positively related to EPS and return. MB is not significantly related to the proportion of restricted stock grants to total compensation while the coefficient on ROA is significantly negative only for the case of Executive 1. The next model, which is Model 5, is: OGPit = i + 1i ROAit + 2i EPSit + 3i MBit + 4i RETit + i (8) OGPit = the proportion of option grants (valued using Black-Scholes model) to total compensation of firm i in year t, ROAit = return on assets of firm i in year t, EPSit = earnings per share of firm i in year t, MBit = market-to-book ratio of firm i in year t, RETit = return on firm i in year t.

Table 15: Regression Results of Model 5 (Dependent Variable: OGPit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.309425884 0 0.277942126 0 0.271762165 0 ROA 0.080419485 0.018410659 0.138293635 1.91381E-05 0.126670981 4.82243E-05 MB 0.009847074 3.51245E-28 0.010806441 8.30029E-38 0.011082942 1.84391E-42 EPS -0.00660058 3.99098E-09 -0.00561235 5.4476E-09 -0.00423321 4.41437E-06 RET -0.04380166 2.70143E-09 -0.03048432 1.2535E-05 -0.03141457 2.75203E-06

176

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

Table 15 suggests that the proportion of option grants value to total compensation is positively related to ROA and MB, but has negative relations with EPS and RET. Subsequently, I try to analyze the pay-performance relationship using lagged values. Conjectures suggest that the effect of performance on compensation may not materialize in contemporaneous year, but will take effect in the following year. This analysis is conducted in Model 6 below. TCit = i + 1i ROAi ,t -1 + 2i EPSi ,t -1 + 3i MBi ,t -1 + 4i RETi ,t -1 + i (9) TCit = total compensation of firm i in year t, ROAit = return on assets of firm i in year t-1, EPSi,t-1 = earnings per share of firm i in year t-1, MBi,t-1 = market-to-book ratio of firm i in year t-1, RETi,t-1 = return on firm i in year t-1.

Table 16: Regression Results of Model 6 (Dependent Variable: TCit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 3293.335145 6.5138E-280 1754.404214 1.3175E-190 1288.078009 1.1011E-195 LagROA 376.9107509 0.483954162 -145.2364934 0.680843354 30.95541209 0.903395139 LagMB 141.8682962 3.94047E-27 103.9161923 2.62729E-33 72.84824061 7.80235E-32 LagEPS 108.4755571 2.18812E-09 107.3606626 2.4809E-23 54.38760541 2.23988E-12 LagRET 1122.13523 2.70466E-24 632.6667299 1.8536E-18 524.8622999 5.27684E-24

It is found that the relationships between total compensation and lagged values of MB, EPS, and return are positive. However, coefficient on ROA is not significant. Furthermore, the coefficients on MB, EPS, and return are stronger for higher level executives. In the next model, changes in compensation and performance variables are brought into regression analysis. TCit = i + 1i ROAit + 2i EPSit + 3i MBit + 4i RETit + i (10) TCit = (TCit ­ TCi,t-1) / TCi,t-1 ROAit = (ROAit ­ ROAi,t-1) / ROAi,t-1 EPSit = (EPSit ­ EPSi,t-1) / EPSi,t-1 MBit = (MBit ­ MBi,t-1) / MBi,t-1 RETit = (RETit ­ RETi,t-1) / RETi,t-1

Table 17: Regression Results of Model 7 (Dependent Variable: TCit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.413552868 1.8302E-124 0.394622937 1.09786E-76 0.329221981 2.8783E-87 ROA 4.22714E-05 0.806127818 -8.17041E-05 0.700988778 -7.5553E-05 0.648668341 MB 0.002665923 0.596580931 0.000661186 0.915342256 0.002386436 0.622553338 EPS 0.000958937 0.454306421 4.71248E-05 0.975690877 0.001162189 0.398041221 RET -1.53697E-09 0.825722083 9.76315E-11 0.9909664 1.60516E-09 0.811218955

I do not find significant associations between the change in total compensation and the changes in ROA, MB, EPS, and return. I then analyze the relationship of pay to performance for firms by taking into account their comparisons to mean values in industry category they belong to. TCIN it = i + 1i ROAIN it + 2i EPSIN it + 3i MBIN it + 4i RETIN it + i (11) TCINit = total compensation of firm i in year t divided by mean TC of industry category (that it belongs to) in year t,

177

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

ROAINit = return on assets of firm i in year t divided by mean ROA of industry category (that it belongs to) in year t, EPSINit = earnings per share of firm i in year t divided by mean EPS of industry category (that it belongs to) in year t, MBINit = market-to-book ratio of firm i in year t divided by mean MB of industry category (that it belongs to) in year t, RETINit = return on firm i in year t divided by mean RET of industry category (that it belongs to) in year t.

Table 18: Regression Results of Model 8 (Dependent Variable: TCINit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.889370878 0.00 0.874047502 0.00 0.88718475 0.00 ROAIN 0.055598445 2.42481E-05 0.028880222 0.050898413 0.026158887 0.07154337 MBIN 0.017382805 0.066850411 0.035986067 0.000618506 0.030978891 0.002567313 EPSIN 0.017854973 7.20878E-06 0.039299067 6.7304E-22 0.034667469 2.35162E-18 RETIN -2.32493E-05 0.784252496 0.000238445 0.488561524 0.000947482 0.001643638

Results in Table 18 report that total compensation relative to industry category compensation has significantly positive relationships with ROA, MB, and EPS relative to their industry category ROA, MB, and EPS. However, the coefficient on RETIN is only significant for the case of Executive 3. Ang et al. (2002) included size effect into pay-performance sensitivity and elasticity analyses. Hence, this study tries to accommodate this issue by creating dummy variables based on size ranks discussed above. These dummy variables are interacted with ROA, MB, EPS, and return variables. This regression consequently has no intercept to avoid dummy variable trap. TCit = 1i ROAit * DRank1t + 2i ROAit * DRank 2t + 3i ROAit * DRank 3t + 4i EPSit *

DRank1t + 5i EPSit * DRank 2t + 6i EPSit * DRank 3t + 7i MBit * DRank1t + 8i MBit * DRank 2t + 9i MBit * DRank 3t + 10i RETit * DRank1t + 11i RETit * DRank 2t +

(12)

12i RETit * DRank 3t + i

TCit = total compensation of firm i in year t, ROAit = return on assets of firm i in year t, EPSit = earnings per share of firm i in year t, MBit = market-to-book ratio of firm i in year t, RETit = return on firm i in year t, DRank1t = dummy variable taking value of 1 if size rank is 1 in year t, 0 otherwise, DRank2t = dummy variable taking value of 1 if size rank is 2 in year t, 0 otherwise, DRank3t = dummy variable taking value of 1 if size rank is 3 in year t, 0 otherwise.

Table 19: Regression Results of Model 8 (Dependent Variable: TCit)

Exec. 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value roarank1 5,572.80 0.00 2,936.75 0.00 2,184.87 0.00 roarank2 10,746.73 0.00 5,712.37 0.00 4,052.71 0.00 roarank3 mbrank1 mbrank2 mbrank3 epsrank1 epsrank2 epsrank3 retrank1 retrank2 retrank3 27,851.67 222.64 220.70 164.91 61.29 114.62 282.85 715.12 1,473.32 2,464.44 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.00 0.00 14,355.47 122.18 96.89 152.72 31.82 145.54 152.31 419.74 629.84 1,616.46 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 10,823.86 88.93 79.92 98.72 29.72 87.71 104.01 298.72 449.50 1,011.33 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00

Table 19 indicates that coefficients on ROA, MB, EPS, and return are stronger for firms in higher size rank (rank 3) than in lower size rank (rank 1). Two exceptions here are: (1) MB for

178

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

Executive 1 shows a descending pattern from size rank 1 to size rank 3 and (2) coefficients on EPS*DRank1 are insignificant. Finally, I check whether executive compensation is affected by performance indicators in the previous year. RETit = i + 1i Salaryi ,t -1 + 2i Bonusi ,t -1 + 3i RSGi ,t -1 + 4i OGi ,t -1 + i (13) Salaryit = executive salary of firm i in year t, Bonusit = executive bonus of firm i in year t-1, RSGi,t-1 = restricted stock grants of firm i in year t-1, OGi,t-1 = option grants valued using Black-Scholes method of firm i in year t-1, RETit = return on firm i in year t.

Table 20: Regression Results of Model 8 (Dependent Variable: RETit)

Executive 1 2 3 Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.127489733 9.99918E-31 0.128785616 2.44282E-36 0.131805216 1.07354E-33 LagSalary 2.61837E-05 0.137271162 3.05762E-05 0.207760342 3.18719E-05 0.321197056 LagBonus 5.5088E-06 0.296790505 9.19171E-06 0.240468421 1.25203E-05 0.365736744 LagRSG 2.08044E-06 0.614983696 -4.88641E-06 0.483848649 2.32803E-06 0.80208671 LagOG -7.3354E-06 1.77557E-05 -7.6027E-06 0.001325987 -1.0962E-05 0.001372069

Findings suggest that return is negatively and significantly affected by lagged value of option grants (valued using Black-Scholes method).

5. Conclusion

This paper empirically finds that total compensation is positively related to ROA, MB, EPS, and return. In addition, the coefficients on the performance variables are stronger for highest paid executives. The proportion of salary to total compensation is negatively and significantly related to ROA, MB, EPS, and return. It implies that the better the performance of a company, the less important will be its executive compensation in the form of basic salary. Model 3 provide evidence that the relationships between the proportion of bonus to total compensation and ROA, EPS, and return are positive and significant while MB is negatively associated with the proportion of bonus to total compensation. Subsequently, the proportion of restricted stock grants to total compensation is found to be positively related to EPS and return. MB is not significantly related to the proportion of restricted stock grants to total compensation while the coefficient on ROA is significantly negative only for the case of Executive 1. Meanwhile, the proportion of option grants value to total compensation is positively related to ROA and MB, but has negative relations with EPS and RET. It is found that the relationships between total compensation and lagged values of MB, EPS, and return are positive. However, coefficient on ROA is not significant. Furthermore, the coefficients on MB, EPS, and return are stronger for higher level executives. However, I do not find significant associations between the change in total compensation and the changes in ROA, MB, EPS, and return. Table 18 reports that total compensation relative to industry category compensation has significantly positive relationships with ROA, MB, and EPS relative to their industry category ROA, MB, and EPS. However, the coefficient on RETIN is only significant for the case of Executive 3. Subsequently, it is shown in Table 19 that the coefficients on ROA, MB, EPS, and return are stronger for firms in higher size rank (rank 3) than in lower size rank (rank 1). Two exceptions here are: (1) MB for Executive 1 shows a descending pattern from size rank 1 to size rank 3 and (2) coefficients on EPS*DRank1 are insignificant. Eventually, findings suggest that return is negatively and significantly affected by lagged value of option grants (valued using Black-Scholes method).

179

European Journal of Economics, Finance and Administrative Sciences - Issue 28 (2011)

Acknowledgement

I am grateful for comments from Dr. Jason Greene and Dr. David Rakowski.

References

1] 2] 3] 4] 5] Ang, J., B. Lauterbach, and B. Schreiber. 2002. Pay at the executive suite: How do U.S. banks compensate their top management teams? Journal of Banking and Finance 26, p.1143-1163. Bebchuk, L. and J. Fried. 2004. Pay without performance: Overview of the issues, Working paper, Harvard University. Bebchuk, L. and Y. Grinstein. 2005. The growth of executive pay, Working paper, Harvard University. Becker, B. 2006. Wealth and executive compensation, Journal of Finance 60 (1), p. 379-397. Benson, B. and W. Davidson. 2008. The relation between stakeholder management, firm value, and CEO compensation: A test of enlightened value maximization, Working paper, Southern Illinois University at Carbondale. Crawford, A., J. Ezzell, and J. Miles. 1995. Bank CEO pay-performance relations and the effects of deregulation, Journal of Business 68 (2), p. 231-256. Farrell, K., F. Geoffrey, and P. Hersch. 2008. How do firms adjust director compensation? Journal of Corporate Finance 14, p. 153-162. Fields, L. and D. Fraser. 1999. On the compensation implications of commercial bank entry into investment banking, Journal of Banking and Finance 23, p. 1261-1276. Hubbard, R. and D. Palia. 1995. Executive pay and performance: Evidence from the U.S. banking industry, Journal of Financial Economics 39, p. 105-130. Rajgopal, S., T. Shevlin, and V. Zamora. 2006. CEOs' outside employment opportunities and the lack of relative performance evaluation in compensation contracts, Journal of Finance 61 (4), p. 1813-1844.

6] 7] 8] 9] 10]

Information

17 pages

Report File (DMCA)

Our content is added by our users. We aim to remove reported files within 1 working day. Please use this link to notify us:

Report this file as copyright or inappropriate

4788


You might also be interested in

BETA
USNAVHOSP OKINAWAINST 7420
2009 Instruction 1120
Microsoft Word - BEJ-31_Vol2011
I