econometrics project the factors affecting the differences in ceo salary from 1989 to 1990

In this paper, given the data set “ceosall”, we will investigate the three factors, which are sales, return on equity roe and return on firm’s stock ros that can be used to explain the d

HANOI UNIVERSITY FACULTY OF BUSINESS ADMINISTRATION & TOURISMS

ECONOMETRICS PROJECT The factors affecting the differences in CEO salary from 1989 to 1990

Group : Bui Tran Khanh Chi Student ID; 1504000008

Nguyễn Thị Thanh Kiều Student ID: 1504000040 Ngô Thúy Quỳnh Student ID: 1504000081

Tutorial class: Tut 4

Submission date: 18/05/2018

Hanoi, May, 2018

TABLE OF CONTENT TABLE OF APPENDICES 0000.000 occ ccccccccccecceceeeeeecesensesceseseeeesesecseseseesecesieaeienscenssaeeesetees il LIST OF TABLES AND FIGURES - S2 22101 221221111 HH He 11

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1 Theoretical Foundation ố 1

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1.2 Executive remuneration 2.3.0.0 ốe 2

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TABLE OF APPENDICES Appendix A: List of formulas

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LIST OF TABLES AND FIGURES Tin — lin model a 5 log — lin model ice cecccecccesesecsscsecsevsecsessessssessscsessessessesessessesssesecsessessessessssasseesas 5 lin — log mod€ÌÏ 211 11211 1121111 2111121101111 10111111 11111 11 H1 11 0111101111111 kg 5

log — log model after dropping “ÝTOS” cv t1 1 11 9111101181101 181111101111 trkg 7 Regression with dummy var1abÌ€§ - tt 11 2112111111101 1811 1101115111151 xe 8 Variance-imflation factor method - - c s21 12111 121111111111111 1111110111 He 10 Park Heteroscedasticrty Test on Log[(og(sales)] co 2x2 22x srrrxee 11

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I Introduction CEO compensation structure is a key component in the corporate governance structures of firms and the process to obtain the information requires a lot of research and analysis for human resource department It is realized that the CEO salaries vary widely between different firms in the same area Different criteria are set to determine the appropriate salary level for CEOs and among that the firm performance can be regarded as an important indicator The question is how they affect the CEO compensation and related to each other In this paper,

given the data set “ceosall”, we will investigate the three factors, which are sales, return on

equity (roe) and return on firm’s stock (ros) that can be used to explain the differences in CEO salaries and discuss the relationships between CEO compensation and these variables from the point of practical relevance, this study will bring a great contribution to regulators and companies in having a better understanding of how the CEO salary or the CEO compensation in general related to the company's performance, thus giving insights in how the paying structure of a firm based on pay-for-performance method can act as a means to tackle agency problems If there is a positive relationship between three variables and positive influence on salaries, the firm can have a more detailed picture of salary variation and consider making appropriate investment decisions for each scenario The purpose of our report is trying to solve three main questions: Firstly, what are the relationships between CEO salaries and sales, return on equity as well as return on firm’s stock? Secondly, are there any comnections among these factors and how do they affect the salaries? And lastly, in the model, are there any errors detected and how to deal with this problem?

II Literature Review 1 Theoretical Foundation This paper studies whether the firm performance can impact on the variation of CEO salary CEO salary is one type of financial incentives that is included in the Executive compensation (Anon, 2018) Several literatures regarding this topic will be reviewed as followings:

1.1 Agency Problem According to Jensen and Meckling (1986), the agency relationship is "a contract which one or more persons (the principal(s)) engage another person (the agent) to perform some service on their behalf which involves delegating some decision-making authority to the agent." This relationship can be the relationship between CEOs (the principal) and shareholders (the agent), in which the CEO is given some authority to act in the way that maximizing best interest of shareholders However, each party always focus on maximizing

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their own utilities and it may arise a conflict (also known as "Agency problem") of interests between the needs of the principal and the needs of the agent As the agency problem may result in conflicts and misunderstanding between two parties, different mechanisms have been found to help solve it (Jensen and Meckling, 1976) For instance, it can be minimized by

ownership structure (Jensen and Meckling, 1976; Fama and Jensen, 1983), the executive

remuneration contracts (Jensen and Murphy, 1990; Lambert et al., 1991) and financial structure of the firm (Easterbrook, 1983; Jensen, 1986)

1.2, Executive remuneration According to Shavell (1979), various forms of compensation provide different incentive effects on CEOs The relationship between agency problem and CEO compensation can be explained by two different theories: optimal contract theory and managerial power of theory (Bebchuk and Fried, 2003) Under the optimal contract theory (OCT), CEO compensation can be seen as a remedy to the agency problem OCT suggests that boards, working in shareholders’ interest, try to provide efficient incentives to managers through their designed compensation schemes that eventually will help them to maximize shareholder value Under the managerial power of theory, managers have more power than the boards, and thus, they have greater ability to extract higher compensation

2 Empirical Findings: The relationship between executive compensation and agency theory can be found in several empirical research such as (Lambert et al.,1991; Gray and Cannella, 1997) Empirical evidence argued that there is a reduction of the agency problem when the compensation of the manager is tied to the stock price (Lambert et al.,1991) In addition, Gray and Cannella (1997) found that incentive compensation can be considered as a tool that aligns the interest of both the principal and agent Besides, empirical studies which have examined the link between firm performance and CEO pay stated that the structure of CEO compensation differs from company to company (Murphy, 1999; Arya and Huey-Lian, 2004) Murphy (1999) documented that if the CEO meets the performance target, the CEO will receive the bonus which is usually determined as a given percentage of his salary There are conflicting results of CEO compensation and firm performance Tosi et al (2000) site the findings of two different set of studies One study by Finkelstein and Boyd (1998) report a significant correlation between Return on Equity and cash compensation of only 0.13 and supporting this finding, Johnson (1982) reported the significant correlation of 0.003 between the two variables To sum up all, in line with existing theory, findings of empirical studies suggested that the compensation contract is a useful mechanism for resolving the agency problem and

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compensation policy is one of the most important factors in an organizations success (Fama, 1980)

In terms of model specification, among many factors leading to the salary volatility, this research emphasizes on two typical categories: Internal factors: sales, ROE (Return on Equity) and ROS (Return on Sale); External factors (Dummy variables): Industrial firms, Financial Firms, Consumer Product Firms or Transport or Utilities Originally, the equation of our model expressed Salary with Internal factors under linear relationship

Salary = Bi+ Be * Sales + Bs * ROE H- Bs * ROS +u

In which variables are: a Dependent variable: Salary (Y) We obtained Salary as Y (thousand $) of 209 observations in 1990 It is apparently difficult to identify one specific salary figures for each career, they vary dramatically based on numerous factors All of them will have certain effects on Salary, therefore, by analyzing the factors, employees can have a general view about salary fluctuation to make suitable expenses or investment decisions

b Independent variable: Sales Which measures revenue (million $) gained from firm’s volume of sales in 1990 The more effective and efficient the performance is, the higher the sale figure

Expectation: a positive relationship between Salary and Sales c Independent variable: ROE (return on equity)

ROE — Net income (earnings)/Equity of shareholders ROE is the ratio of net profit to equity, which reflects the ability to use the capital of the business for profit “This index is an accurate measure of how much a return is made and how much accrued interest it generates and it is often analyzed by investors for comparison with other peers in the market when they decide to buy shares of any company” Dr Quynh said The higher the ROE is, the more effectively the company uses equity capital So why can the ROE affect to salary? When the ROE increases significantly, it means that the

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company and its employees are operating efficiently This can influence positively to staffs’ wages, maybe, the managers will raise their salary following a multiplier 1

Expectations: We expect a positive relationship between salary and ROE d Independent variable: ROS (return on sale)

ROS — Net profit after Tax / Sales ROS indicates how much profit accounted for in revenue This ratio means that the company is profitable, the larger the ROS is, the greater the profit is So why should we consider ROS as a variable that impacts in salary? Because when ROS increases, the profit a company receives after exchanging also goes up It is cheerful news for a corporation and perhaps, they will have some bonuses for their employees adding to salary

Expectations: We expect a positive relationship between salary and ROS In terms of research design, the research follows the non-experimental research design It identifies the relationship among salary and other factors The hypothesis testing and other analysis also revolve around these relationships

There are various tests used in this study in order to make the concrete conclusion of the report In multiple regressions, we conducted 4 tests including testing the overall significance of all coefficients, the functional form of regression model and the dummy variable We also check the errors including multicollinearity, heteroscedasticity, and autocorrelation

IV Data Analysis and Results 1 Model testing

1.1 Testing on functional form In this part, we will run the OLS on three types of functional form that is lin-lin model, semi-log model (lin-log & log-lin model), and log-log model to figure out the best linear unbiased estimator (BLUE) regression by using Eviewl0 However, with the “ceosall” data set given, we cannot run the /og/ros) because of non-positive number in ros figures in the excel file Therefore, it is obligated that we only obtain roe in lin-log model and log-log model Through running four models, we will obtain R-squared (R?) and covariance (CV) of each model and then choose the optimal model with highest R-squared and lowest CV as followings

a Lin-lin model: Salary= f+ f2*sales+ B;*roet+ Bi*ros + u

Method: Least Squares Date: 05/05/18 Time: 16:26 Sample: 1 209 Included observations: 209

Variable Coefficient Std Error tStatistic Prob Cc 864.1175 228.3997 3.783356 0.0002 SALES 0.015482 0.008954 1.729131 0.0853 ROE 22.00331 11.51655 1.910581 0.0575 ROS -1.105191 1.450240 -0.762074 04469 R-squared 0.031914 Mean dependent var 1281.120 Adjusted R-squared 0.017747 S.D dependent var 1372.345 S.E of regression 1360.113 Akaike info criterion 17.28748 Sum squared resid 3.79E+08 Schwarz criterion 17.35144 Log likelinood -1802.541 Hannan-Quinn criter 17.31334 F-statistic 2.252699_ Durbin-VVatson stat 2.118133 Prob(F-statistic) 0.083369 Figure d: lin — lin model

> Obtaining R’= 0.0319, CV = 1.06166 b Log-lin model: Log(salary)= B1+ ÿ2*sales+ J3 *roe+ 4 *ros + u

Dependent Variable: LOG(SALARY) Method: Least Squares Date: 05/05/18 Time: 16:29 Sample: 1 209 Included observations: 209

Variable Coefficient Std Error tStatistic Prob Cc 6.611921 0.088860 74.40868 0.0000 SALES 1.50E-05 3.48E-06 4.294896 0.0000 ROE 0.016833 0.004481 3.757009 0.0002 ROS -0.000880 0.000564 -1.559877 0.1203 R-squared 0.139699 Mean dependent var 6.950386 Adjusted R-squared 0.127109 S.D dependent var 0.566374 S.E of regression 0.529156 Akaike info criterion 1.583885 Sum squared resid 57.40118 Schwarz criterion 1.647853 Log likelihood -161.5160 Hannan-Quimn criter 1.609748 F-statistic 11.09619 Durbin-Watson stat 1.980010

> Obtaining R’?= 0.1397, CV = 0.07613 ce Lin-log model: Salary= 1+ 2*log(sales)+ 3 *log(roe)+ P4*ros +u

Dependent Variable: SALARY Method: Least Squares Date: 05/05/18 Time: 16:30 Sample: 1 209 Included observations: 209

Variable Coefficient Std Error t-Statistic Prob Cc -1996.902 991.8855 -2.013239 0.0454 LOG(SALES) 290.5537 98.38641 2.953190 0.0035 LOG(ROE) 312.2340 174.9482 1.784722 0.0758 ROS 0.312329 1.484193 0.210437 0.8335 R-squared 0.053717 Mean dependert var 1281.120 Adjusted R-squared 0.039869 S.D dependent var 1372.345 S.E of regression 1344.710 Akaike info criterion 17.26470 Sum squared resid 3.71E+08 Schwarz criterion 17.32867 Log likelinood -1800.161 Hannan-Quinn criter 17.29056 F-statistic 3.879034 Durbin-Watson stat 2.135835

> Obtaining R’*=0.0537, CV= 1.0496 d Log-log model: Log(salary)= 1+ 2*log(sales)+ B3*log(roe)+ P4*ros + u

Method: Least Squares Date: 05/05/18 Time: 16:20 Sample: 1 209 Included observations: 209

Adjusted R-squared 0.251521 S.D dependent var 0.566374

S.E of regression 0.489997 Akaike info criterion 1.430118

Sum squared resid 49.21987 Schwarz criterion 1.494086 Leg likelinood -145.4473 Hannan-Quinn criter 1.455981

Log(salary)= B+ B2*log(sales)+ :*log(roe)+ Bi*res + u 1.2 Testing the overall significance on all coefficients We have the equation of log-log model:

Ln(salary)= B+ B2*log(sales)+ f› *log(roe)+ f¡*ros + u e Stating hypothesis: