The Pay of the CEO in the Organization - Research Paper Example

Chief Executive Officers are the core of every organization and mostly, they determine what the company/organization does at the end of every financial year. It has been argued that, the higher the CEOs pay, the good the returns of that organization. However, this has been disputed in many instances by people arguing that, a CEO may get very little but if he/she has the organization in the heart, then he/she will take it a notch higher despite a little pay. In this regard, data on 149 companies was collected in 2002 to determine whether the pay of the CEO really has anything to do with the organizations/company’s performance. Further, the data collected was used to determine the amount the CEOs pay contributes towards the organization’s rating. The data had several variables namely; pay, total, return rating and ratio. Regression analysis was carried out using STATA and the results follow. Return = 104.89 + .000447 Pay …………………………………………1 Introduction According to David, K. and Ward, D. (2006), people tend to believe that CEOs are paid too much for what they do and for the services they provide. At the same time according to them, others believe that, a CEO can have a positive effect on the performance of the organization and, therefore, high compensation is needed to attract the best talent (David, K. & Ward, D. 2006). Judging which CEO are overpaid or underpaid is a huge task (Gough, J 2000). Many articles as Gough, J (2000) focus on high CEO pay where the simply survey the executives who have or have been receiving the most overall money in a particular year. In addition many indirect perquisites of the various organizations are possibly not included in the final figures which are said to be the CEO’s pay. In stocks for example, the CEO is allowed to buy the stock at a certain value later down the road (Macdonald, N 1999). To him, if the CEO makes the companies do well, then the price of the stock (for example) down the road can be much higher and the CEO could buy shares for his option price. Defenders of high executive pay say that the global war for talent and the rise of private companies/firms can fairly explain much of the increase in executive pay (Beech, W 2001). For example, in Japan, a senior executive has very few alternatives for his/her current employer while in the United States, it is totally acceptable and even many admire this, for a senior executive to leave an organization to a competitor; either to a private firm (Carey, C 2002). Many questions therefore arise as to whether; the figures reflect an efficient market, or a failed one, whether the pay levels adequately disclosed, whether shareholders should have a more say and lastly, whether there is fairness and justice (Alan C. 2003). According to Schmidt, P. (1996), some hedge fund managers make $400 million a year and others more than these. Mostly according to him, more through arrangements that bring their firms 30% of the annual profits the organization achieves To Wooldridge, J. (2006), if you do that year after year, you become a billionaire very quickly. According to him, some CEOs even do not know how much the firm is compensated overall, but what he/she is getting thus; there is very little transparency as far as their contribution towards the overall wellbeing of the organization is concerned. All in all, some transparency do exists at publicly traded firms. This has led to considerable controversy in recent years since this is seen by many as executives try to get richer while pay for lower level workers has continuously remained relatively stagnant. A study done by Business Week shows that, compensation for big company CEOs is more than 400 times the pay for average workers last year (2007), which is up from a 42-to-1 ratio in 1980 (Carey, C 2002). He concludes by saying that, if the minimum wage had gone up at the same rate both for the senior management and the low level cadre staff, then it would have been more than $23 an hour in 2006 instead of $5.15. A business can be undermined when the ratio of executive pay to worker pay gets too high (Carey, C 2002). This is so because, morale falls as employees are tired of being asked to make sacrifices not shared by their bosses (in terms of pay). Carey predicts that, if companies do not get executive pay under control, they will face the threat of breaking down or loosing financially. In addition he suggests that, companies/organization should set pay limits before searching for new executives and they should be more skeptical of data showing pay at similar companies. The rate o return in any organization should be proportional to the way the CEO and other top management officers perform. An organizations future depends entirely on how these officials perform. Company rating which is another thing organizations look for is also vested in the way the officials operate. Results and Explanations The data collected shows that most of the CEO earns about 7,927. Further, the returns for the organization they work for range from 105, 000,000. 1. Regression of return and pay Source SS df MS Number of obs = 149 F (1, 147) = 0.03 Model 95.2822404 1 95.2822404 Prob > F = 0.8636 Residual 472630.53 147 3215.17367 R-squared = 0.0002 Adj R-squared = -0.0066 Total 472725.812 148 3194.09332 Root MSE = 56.703 From the above table, it is evident that the amount of pay determines the returns a certain organization gets from the CEO at 95% level of significance although the two are negatively related with each other Coef. Std. Err. t P>|t| [95% Conf. Interval] Pay .0000447 .0002594 0.17 0.864 -.000468 .0005573 Cons 104.8943 5.080158 20.65 0.000 94.8547 114.9339 The coefficient for the pay is not significant at all but the constant is. The coefficient for the pay lies between, -.00468 and .0005573 with a standard error of .00025 while the constant lies between 94.85 and 114.93 with an error of 5.080. Below is the regression equation, Return = 104.89 + .000447 Pay …………………………………………1 2. Regression of return and total pay Source SS df MS Number of obs = 149 F (1, 147) = 9.59 Model 28936.958 1 28936.958 Prob > F = 0.0023 Residual 443788.854 147 3018.9718 R-squared = 0.0612 Adj R-squared = 0.0548 Total 472725.812 148 3194.09332 Root MSE = 54.945 From the ANOVA table above, the rate of return and the total pay are significantly different from each other at 95% CI. Both have a positive weak relationship with each other. Coef. Std. Err. t P>|t| [95% Conf. Interval] Total -.0003867 .0001249 -3.10 0.002 -.0006335 -.0001399 Cons 115.165 5.524612 20.85 0.000 104.2471 126.0829 Both coefficients; the coefficient for the total pay and the constant, are significant and they contribute towards the organizations returns at 95% significance. The coefficient for the total pay lies between -.0006335 and -.0001399 with an error of .0001249 while the constant lies between 104.247 and 126.0829 with an error of 5.52. The regression equation can be formulated as below; Return = 115.165 - .00038 Total Pay……………………………………….2 3. Regression of rating and total pay for the CEOs Source SS df MS Number of obs = 149 F (1, 147) = 79.86 Model 69.0121689 1 69.0121689 Prob > F = 0.0000 Residual 127.0281 147 864136732 R-squared = 0.3520 Adj R-squared = 0.3476 Total 196.040268 148 1.32459641 Root MSE = .92959 ANOVA depicts that, the rating of the company’s rating and the pay the CEO gets are significantly different at 95% CI Coef. Std. Err. t P>|t| [95% Conf. Interval] Total -.0000189 2.11e-06 -8.94 0.000 -.0000231 -.0000147 Cons 3.625226 .0934681 38.79 0.000 3.440511 3.809941 The two coefficients are significant in the prediction of the rating of the organization. The coefficient foe the total pay lies between -.000231 and -.0000147 and an error of 0.00000211 Exists while the constant lies between 3.44 and 3.8 an error of .0934 exists. The regression line which can be used to say the rating of the organization based on the total pay. Rating = 3.625 - .0000189 Total ……………………………………..3 4. Regression of rating and return Source SS df MS Number of obs = 149 F (1, 147) = 43.20 Model 44.5242735 1 44.5242735 Prob > F = 0.0000 Residual 151.515995 147 1.03072105 R-squared = 0.2271 Adj R-squared = 0.2219 Total 196.040268 148 1.32459641 Root MSE = 1.0152 The rating and the return of an organization are both significant and are positively related at 95% CI. Coef. Std. Err. t P>|t| [95% Conf. Interval] Return .009705 .0014766 6.57 0.000 .0067868 .0126231 Cons 2.119509 .1762671 12.02 0.000 1.771164 2.467854 Both of the coefficients are significant at 95% level of significance. That for the return lies between .0068 and .012 with an error of .0014 while that of the constant lies between 1.77 and 2.46 with an error of .1762. The return of the organization can be used to estimate the rating using the following equation. Rating = 2.11 + .0097 Return……………………………………….4 4. Regression of the return and ratio of employees pay Source SS df MS Number of obs = 149 F (1, 147) = 19.10 Model 54360.78 1 54360.78 Prob > F = 0.0000 Residual 418365.032 147 2846.02063 R-squared = 0.1150 Adj R-squared = 0.1090 Total 472725.812 148 3194.09332 Root MSE = 53.348 The ANOVA shows that, the other employees pay ratio to that of the top management brass are both resulting into different return at 95% CI and are weakly related to each other. Coef. Std. Err. t P>|t| [95% Conf. Interval] Ratio .8455539 .1934717 4.37 0.000 .4632087 1.227899 Cons 93.95064 5.077714 18.50 0.000 83.9159 103.9854 Both of the coefficients are significant. The return coefficient ranges from .46 to 1.22 while the constant ranges from 83.91 and 103.91. The prediction equation is as below. Return = 93.95 + .84 Ratio…………………………………5 Figure 1: Scatter Plot The figure above shows that, it is not a must for the pay to be good for the CEO to perform. Figure 2: Scatter Plot Conclusion It is clear that, the pay of the CEO in any organization does not always mean that the organization will do well. Their should be an organization that should specifically deal with the top level management pay since if this is not checked, then they will be drawing more than what the organization can produce at the end of the contract period. This should be gauged by the way the CEO performs since the rating of the organization in the ladder will definitely depend of what the returns are and not what the CEO earns at the end of the period. The ration between the junior staffs pay and that of the senior level management has got a lot to do with the level of returns since the poor pay juniors get make them to feel unwanted and tend to perform not to the expectations thus making the organization to earn little. Reference: Alan C. (2003). Keynesian Economics: The First Principles. New York: Rout ledge publishers Beech, W (2001). Against all odds: Ten entrepreneurs who followed their hearts and found success. New York: John Wiley Carey, C (2002). American inventors, entrepreneurs, and business visionaries. New York: Facts on File David, K. & Ward, D. (2006). Economics for Business: McGraw-Hill Publishing Co. Gough, J (2000). Introductory Economics for Business and Management. United Kingdom: McGraw-Hill Macdonald, N (999). Macroeconomics and Business: An Interactive Approach: Thomson Learning Schmidt, P. (1996). Econometrics: Marcel Decker publishers, New York Wooldridge, J. (2006). Introduction to econometrics, 3rd edition, MIT publishers, London Appendix regress return pay Source | SS df MS Number of obs = 149 -------------+------------------------------ F( 1, 147) = 0.03 Model | 95.2822404 1 95.2822404 Prob > F = 0.8636 Residual | 472630.53 147 3215.17367 R-squared = 0.0002 -------------+------------------------------ Adj R-squared = -0.0066 Total | 472725.812 148 3194.09332 Root MSE = 56.703 ------------------------------------------------------------------------------ return | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- pay | .0000447 .0002594 0.17 0.864 -.000468 .0005573 _cons | 104.8943 5.080158 20.65 0.000 94.8547 114.9339 ------------------------------------------------------------------------------ . regress return total Source | SS df MS Number of obs = 149 -------------+------------------------------ F( 1, 147) = 9.59 Model | 28936.958 1 28936.958 Prob > F = 0.0023 Residual | 443788.854 147 3018.9718 R-squared = 0.0612 -------------+------------------------------ Adj R-squared = 0.0548 Total | 472725.812 148 3194.09332 Root MSE = 54.945 ------------------------------------------------------------------------------ return | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- total | -.0003867 .0001249 -3.10 0.002 -.0006335 -.0001399 _cons | 115.165 5.524612 20.85 0.000 104.2471 126.0829 ------------------------------------------------------------------------------ . regress rating total Source | SS df MS Number of obs = 149 -------------+------------------------------ F( 1, 147) = 79.86 Model | 69.0121689 1 69.0121689 Prob > F = 0.0000 Residual | 127.0281 147 .864136732 R-squared = 0.3520 -------------+------------------------------ Adj R-squared = 0.3476 Total | 196.040268 148 1.32459641 Root MSE = .92959 ------------------------------------------------------------------------------ rating | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- total | -.0000189 2.11e-06 -8.94 0.000 -.0000231 -.0000147 _cons | 3.625226 .0934681 38.79 0.000 3.440511 3.809941 ------------------------------------------------------------------------------ . regress rating return Source | SS df MS Number of obs = 149 -------------+------------------------------ F( 1, 147) = 43.20 Model | 44.5242735 1 44.5242735 Prob > F = 0.0000 Residual | 151.515995 147 1.03072105 R-squared = 0.2271 -------------+------------------------------ Adj R-squared = 0.2219 Total | 196.040268 148 1.32459641 Root MSE = 1.0152 ------------------------------------------------------------------------------ rating | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- return | .009705 .0014766 6.57 0.000 .0067868 .0126231 _cons | 2.119509 .1762671 12.02 0.000 1.771164 2.467854 ------------------------------------------------------------------------------ regress return ratio Source | SS df MS Number of obs = 149 -------------+------------------------------ F( 1, 147) = 19.10 Model | 54360.78 1 54360.78 Prob > F = 0.0000 Residual | 418365.032 147 2846.02063 R-squared = 0.1150 -------------+------------------------------ Adj R-squared = 0.1090 Total | 472725.812 148 3194.09332 Root MSE = 53.348 ------------------------------------------------------------------------------ return | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ratio | .8455539 .1934717 4.37 0.000 .4632087 1.227899 _cons | 93.95064 5.077714 18.50 0.000 83.9159 103.9854 ------------------------------------------------------------------------------ . . Read More