The document discusses a statistical analysis of factors that influence CEO compensation in large UK businesses. Univariate analyses found CEO salaries were highly skewed and not normally distributed. Bivariate tests showed CEOs who also serve as board chairperson earn significantly higher salaries on average (£481,286.51) than CEOs who do not chair the board (£281,149.70). Independent t-tests found this difference in mean logged salaries between the two groups was statistically significant.
CEO Turnover
By David F. Larcker, Brian Tayan
CGRI Research Spotlight Series. September 2016
This Research Spotlight provides a summary of the academic literature on relation between CEO performance and turnover. It reviews the evidence of:
The relation between performance and likelihood of termination
The relation between board attributes and likelihood of termination
Other factors that might influence CEO performance oversight
This Research Spotlight expands upon issues introduced in the Quick Guide “CEO Succession Planning.”
This study argues that firms can use profit sharing to raise profits by reducing the risk for inefficient separations by the workers. It is based on the idea of Hall and Lazear (1984) and clarifies their analysis of fixed wage contracts in employment relations of fixed length. It extends their analysis to continuing employment relationships in which the base wage is exogenously given but the firm can unilaterally set a profit sharing parameter. It is argued that the use of down payments, on which the analysis of Hall and Lazear is based, is not possible with continuing employment relationships. Instead the bargaining power will be reflected in the base wage. This reduces the possibilities to adjust the base wage to reduce inefficient separations and may increase the importance of profit sharing. The intuitive positive dependence of the profit sharing parameter on the covariance between the value of the worker’s outside option and his productivity in the firm does not necessarily hold.
Staggered Boards
Authors: Professor David F. Larcker and Brian Tayan,
Researcher, Corporate Governance Research Initiative
Stanford Graduate School of Business
This Research Spotlight provides a summary of the academic literature on how staggered boards impact shareholder value by insulating management from the pressures of capital markets.
It reviews the evidence of:
-Staggered board provisions in IPO charters
-The impact of staggered boards on merger activity
-The relation between staggered boards and market value
-Shareholder reaction to a decision to (de)stagger a board
-Firm outcomes following a decision to (de)stagger a board
This Research Spotlight expands upon issues introduced in the Quick Guide “The Market for Corporate Control.”
For an expanded discussion, see Corporate Governance Matters: A Closer Look at Organizational Choices and Their Consequences (Second Edition) by David Larcker and Brian Tayan (2015): http://www.gsb.stanford.edu/faculty-research/books/corporate-governance-matters-closer-look-organizational-choices
Buy This Book: http://www.ftpress.com/store/corporate-governance-matters-a-closer-look-at-organizational-9780134031569
For permissions to use this material, please contact: E: corpgovernance@gsb.stanford.edu
CEO Turnover
By David F. Larcker, Brian Tayan
CGRI Research Spotlight Series. September 2016
This Research Spotlight provides a summary of the academic literature on relation between CEO performance and turnover. It reviews the evidence of:
The relation between performance and likelihood of termination
The relation between board attributes and likelihood of termination
Other factors that might influence CEO performance oversight
This Research Spotlight expands upon issues introduced in the Quick Guide “CEO Succession Planning.”
This study argues that firms can use profit sharing to raise profits by reducing the risk for inefficient separations by the workers. It is based on the idea of Hall and Lazear (1984) and clarifies their analysis of fixed wage contracts in employment relations of fixed length. It extends their analysis to continuing employment relationships in which the base wage is exogenously given but the firm can unilaterally set a profit sharing parameter. It is argued that the use of down payments, on which the analysis of Hall and Lazear is based, is not possible with continuing employment relationships. Instead the bargaining power will be reflected in the base wage. This reduces the possibilities to adjust the base wage to reduce inefficient separations and may increase the importance of profit sharing. The intuitive positive dependence of the profit sharing parameter on the covariance between the value of the worker’s outside option and his productivity in the firm does not necessarily hold.
Staggered Boards
Authors: Professor David F. Larcker and Brian Tayan,
Researcher, Corporate Governance Research Initiative
Stanford Graduate School of Business
This Research Spotlight provides a summary of the academic literature on how staggered boards impact shareholder value by insulating management from the pressures of capital markets.
It reviews the evidence of:
-Staggered board provisions in IPO charters
-The impact of staggered boards on merger activity
-The relation between staggered boards and market value
-Shareholder reaction to a decision to (de)stagger a board
-Firm outcomes following a decision to (de)stagger a board
This Research Spotlight expands upon issues introduced in the Quick Guide “The Market for Corporate Control.”
For an expanded discussion, see Corporate Governance Matters: A Closer Look at Organizational Choices and Their Consequences (Second Edition) by David Larcker and Brian Tayan (2015): http://www.gsb.stanford.edu/faculty-research/books/corporate-governance-matters-closer-look-organizational-choices
Buy This Book: http://www.ftpress.com/store/corporate-governance-matters-a-closer-look-at-organizational-9780134031569
For permissions to use this material, please contact: E: corpgovernance@gsb.stanford.edu
Authors: Professor David F. Larcker and Brian Tayan, Researcher, Corporate Governance Research Initiative, Stanford Graduate School of Business
Other organizational structures exist besides public corporations. Examples include family-controlled businesses, venture-backed companies, private equity-owned businesses, and nonprofit organizations. Each of these faces their own issues relating to purpose, ownership, and control.
This Quick Guide reviews the governance features adopted by these entities.
It provides answers to the questions:
• What are the purposes of these organizations?
• What governance solutions do they adopt?
• How effective are they in meeting their objectives?
For an expanded discussion, see Corporate Governance Matters: A Closer Look at Organizational Choices and Their Consequences (Second Edition) by David Larcker and Brian Tayan (2015): http://www.gsb.stanford.edu/faculty-research/books/corporate-governance-matters-closer-look-organizational-choices
Buy This Book: http://www.ftpress.com/store/corporate-governance-matters-a-closer-look-at-organizational-9780134031569
For permissions to use this material, please contact: E: corpgovernance@gsb.stanford.edu
Copyright 2015 by David F. Larcker and Brian Tayan. All rights reserved.
On November 17, 2018, Justin Falk and Nadia Karamcheva, analysts in CBO’s Microeconomic Studies Division, presented at the National Tax Association’s 111th Annual Conference on Taxation.
This presentation provides information about how CBO estimates the effects of employer matching and default deferral rates on federal employees’ contribution rates to the Thrift Savings Plan and on employers’ costs.
We find that IPO firms with generously compensated CEOs and large pay disparities between the CEO and other top executives have lower failure rates and longer time to survive in subsequent periods following the offering. Economically, firms with CEO pay (pay gaps) in the 75th percentile have a failure risk that is, on average, 11.56% (13.20%) lower than the failure risk of firms with CEO pay (pay gaps) in the 25th percentile. The relationship between CEO pay and IPO survival is strengthened among firms with lower agency conflicts, whereas the link between pay gap and IPO survival is pronounced among firms with stronger internal promotion incentives. The results are robust to alternative specifications and additional sensitivity tests.
Performance pay and employee turnover article analysis Enas Mekki
Performance pay and employee turnover article analysis
Purpose
– The purpose of this paper is to explore how various performance related pay (PRP) schemes influence employee turnover. It also tests whether profit sharing has a differential impact on turnover in comparison to other forms of PRP.
Design/methodology/approach
– Utilizing a nationally representative longitudinal dataset of individuals, analysis begins with a parsimonious specification of the determinants of turnover and then progressively adds various sets of controls known to influence turnover decisions to observe how their inclusion influences PRP coefficients. Estimations employ both standard probits and panel data models.
Findings
– Empirical evidence reveals a negative relationship between an aggregate measure of PRP and turnover. Disaggregating performance pay measures by type reveals a robust negative relationship between profit sharing and turnover. Although one would expect the influence of other PRP schemes to mimic that of profit sharing, evidence suggests otherwise.
Research limitations/implications
– Data lack information on how much earnings are based on PRP. Consequently, estimates may be biased when combining those who receive little earnings from PRP with those who receive substantial amounts of PRP into a single PRP measure.
Practical implications
– Although PRP schemes are often introduced to improve incentives and productivity, profit sharing based on firm profitability may allow labor costs to vary with firm profits hence enhancing retention and reducing the incidence of unemployment during recession.
Originality/value
– This paper adds to the literature and fulfils an identified need to study how other types of PRP besides profit sharing influence turnover.
January 23rd, 2012
What Is CEO Talent Worth?
By Professor, David F. Larcker and Brian Tayan, Researcher, Corporate Governance Research Program, Stanford Graduate School of Business
January 24, 2012
The topic of executive compensation elicits strong emotions among corporate stakeholders and practitioners. On the one hand are those who believe that chief executive officers in the United States are overpaid. On the other hand are those who believe that CEOs are simply paid the going fair-market rate.
Much less effort, however, is put into determining whether total compensation is commensurate with the value of services rendered.
We examine the issue and explain how such a calculation might be performed. We ask:
* How much value creation should be attributable to the efforts of the CEO?
* What percentage of this value should be fairly offered as compensation?
* Can the board actually perform this calculation? If not, how does it make rational decisions about pay levels?
Read the attached Closer Look and let us know what you think!
This case examines seven commonly accepted myths about corporate governance. How can we expect managerial behavior and firm performance to improve, if practitioners continue to rely on myths rather than facts to guide their decisions?
The V Foundation - Reno Hold 'Em & Celeb Ski Event RecapDerek Boyle
A recap of The V Foundation Charity No-Limit Hold 'Em Tournament and Take It Downhill Celebrity Ski Challenge. Includes marketing materials, media coverage, as well as images and captions that portray the 3-day experience.
Authors: Professor David F. Larcker and Brian Tayan, Researcher, Corporate Governance Research Initiative, Stanford Graduate School of Business
Other organizational structures exist besides public corporations. Examples include family-controlled businesses, venture-backed companies, private equity-owned businesses, and nonprofit organizations. Each of these faces their own issues relating to purpose, ownership, and control.
This Quick Guide reviews the governance features adopted by these entities.
It provides answers to the questions:
• What are the purposes of these organizations?
• What governance solutions do they adopt?
• How effective are they in meeting their objectives?
For an expanded discussion, see Corporate Governance Matters: A Closer Look at Organizational Choices and Their Consequences (Second Edition) by David Larcker and Brian Tayan (2015): http://www.gsb.stanford.edu/faculty-research/books/corporate-governance-matters-closer-look-organizational-choices
Buy This Book: http://www.ftpress.com/store/corporate-governance-matters-a-closer-look-at-organizational-9780134031569
For permissions to use this material, please contact: E: corpgovernance@gsb.stanford.edu
Copyright 2015 by David F. Larcker and Brian Tayan. All rights reserved.
On November 17, 2018, Justin Falk and Nadia Karamcheva, analysts in CBO’s Microeconomic Studies Division, presented at the National Tax Association’s 111th Annual Conference on Taxation.
This presentation provides information about how CBO estimates the effects of employer matching and default deferral rates on federal employees’ contribution rates to the Thrift Savings Plan and on employers’ costs.
We find that IPO firms with generously compensated CEOs and large pay disparities between the CEO and other top executives have lower failure rates and longer time to survive in subsequent periods following the offering. Economically, firms with CEO pay (pay gaps) in the 75th percentile have a failure risk that is, on average, 11.56% (13.20%) lower than the failure risk of firms with CEO pay (pay gaps) in the 25th percentile. The relationship between CEO pay and IPO survival is strengthened among firms with lower agency conflicts, whereas the link between pay gap and IPO survival is pronounced among firms with stronger internal promotion incentives. The results are robust to alternative specifications and additional sensitivity tests.
Performance pay and employee turnover article analysis Enas Mekki
Performance pay and employee turnover article analysis
Purpose
– The purpose of this paper is to explore how various performance related pay (PRP) schemes influence employee turnover. It also tests whether profit sharing has a differential impact on turnover in comparison to other forms of PRP.
Design/methodology/approach
– Utilizing a nationally representative longitudinal dataset of individuals, analysis begins with a parsimonious specification of the determinants of turnover and then progressively adds various sets of controls known to influence turnover decisions to observe how their inclusion influences PRP coefficients. Estimations employ both standard probits and panel data models.
Findings
– Empirical evidence reveals a negative relationship between an aggregate measure of PRP and turnover. Disaggregating performance pay measures by type reveals a robust negative relationship between profit sharing and turnover. Although one would expect the influence of other PRP schemes to mimic that of profit sharing, evidence suggests otherwise.
Research limitations/implications
– Data lack information on how much earnings are based on PRP. Consequently, estimates may be biased when combining those who receive little earnings from PRP with those who receive substantial amounts of PRP into a single PRP measure.
Practical implications
– Although PRP schemes are often introduced to improve incentives and productivity, profit sharing based on firm profitability may allow labor costs to vary with firm profits hence enhancing retention and reducing the incidence of unemployment during recession.
Originality/value
– This paper adds to the literature and fulfils an identified need to study how other types of PRP besides profit sharing influence turnover.
January 23rd, 2012
What Is CEO Talent Worth?
By Professor, David F. Larcker and Brian Tayan, Researcher, Corporate Governance Research Program, Stanford Graduate School of Business
January 24, 2012
The topic of executive compensation elicits strong emotions among corporate stakeholders and practitioners. On the one hand are those who believe that chief executive officers in the United States are overpaid. On the other hand are those who believe that CEOs are simply paid the going fair-market rate.
Much less effort, however, is put into determining whether total compensation is commensurate with the value of services rendered.
We examine the issue and explain how such a calculation might be performed. We ask:
* How much value creation should be attributable to the efforts of the CEO?
* What percentage of this value should be fairly offered as compensation?
* Can the board actually perform this calculation? If not, how does it make rational decisions about pay levels?
Read the attached Closer Look and let us know what you think!
This case examines seven commonly accepted myths about corporate governance. How can we expect managerial behavior and firm performance to improve, if practitioners continue to rely on myths rather than facts to guide their decisions?
The V Foundation - Reno Hold 'Em & Celeb Ski Event RecapDerek Boyle
A recap of The V Foundation Charity No-Limit Hold 'Em Tournament and Take It Downhill Celebrity Ski Challenge. Includes marketing materials, media coverage, as well as images and captions that portray the 3-day experience.
North Valleys Transportation Study January 25, 2017This Is Reno
The Washoe Regional Transportation Commission released a draft traffic study that shows growth in the North Valleys will lead to further congestion if measures aren’t taken to address traffic problems.
“Some of the arterials and collectors connecting U.S. 395 and the North Valleys neighborhoods are anticipated experience close to or more than a 200-percent increase,” the report indicated.
Running head Organization behaviorOrganization behavior 2.docxtoltonkendal
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
Welcome to the 2021 Indigo’s C-Level Salary Guide. successful salary planning requires a thorough understanding of factors that influence the amount required to secure the appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper compensation.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Study to investigate the correlation between the operating performances of fi...Charm Rammandala
The purpose of this study to understand whether there is a correlation between the operating performance of a firm and its CEO’s compensation. Various scholars and journalists studied and reported in this area over the years with mixed results. Popular notion among general public is that regardless of the performance of the company, CEO’s pay and perks either remain same or increase. Another accusation is most of the mergers and acquisitions taken place to boost the pay of CEO’s rather than to increase the value of shareholder. Study will look in to the validity of these claims to determine whether there is a correlation between the firm performances and the CEO pay
MARKETING MANAGEMENT PHILOSOPHIES
CHAPTER 1 - ASSIGNMENT
Question 1.
Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions.
In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies.
Be specific with your examples.
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variabl ...
The Analysis of Earning management and Earning Response Coefficient: Empiric...inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Factors Affecting Tax Officials’ Occupational Stress in Binh Thuan Provinceijtsrd
The study aims to determine factors and the impact level of each factor on tax officials’ occupational stress in Binh Thuan Province. Research data were collected from 300 tax officials using convenient sampling. Applying the exploratory factor analysis and multivariate linear regression, the study has proven factors that increase occupational stress of tax officials. Accordingly, they are work nature and organizational characteristics. Otherwise, factors reducing their occupational stress are career development, the organization’s role, and tax officials’ commitments. In particular, commitment has the most influence on their occupational stress. Le Thuy Trang | Dinh Hoang Anh Tuan | Nguyen Quoc Nghi "Factors Affecting Tax Officials’ Occupational Stress in Binh Thuan Province" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38530.pdf Paper Url: https://www.ijtsrd.com/management/business-administration/38530/factors-affecting-tax-officials’-occupational-stress-in-binh-thuan-province/le-thuy-trang
Welcome to the 2019 Indigo’s C-Level Salary Guide. Forming an effective compensation strategy is not as easy as it appears. Some managers habitually throw a dollar figure at an employment contract. However, successful salary planning requires a thorough understanding of factors that influence the amount required to secure the appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring, a look at employment in IT, and several key hiring strategies for 2020.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
The Two-Tier Board System and Underpricing of Initial Public Offerings: Evid...Lalith Samarakoon
We study the relation between initial IPO underpricing and two-tier board structure in the Vienna Stock Exchange of Austria, where a two-tier board is mandatory for listed companies. The board ratio, defined as the size of the supervisory board to the management board, is used to capture the effect of two-tiered board on underpricing. The results show that the board ratio is negatively related with underpricing, consistent with the agency theory which predicts that more effective monitoring implied in a relatively larger supervisory board will lead to lower agency costs, and thus lower underpricing. The results are robust to the inclusion of control variables and suggest that firms seeking to raise external capital will be helped by adopting strong corporate governance standards.
Similar to Can CEO compensation be justified, at least statistically? (20)
5 Tips for Creating Standard Financial ReportsEasyReports
Well-crafted financial reports serve as vital tools for decision-making and transparency within an organization. By following the undermentioned tips, you can create standardized financial reports that effectively communicate your company's financial health and performance to stakeholders.
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the what'sapp contact of my personal pi merchant to trade with
+12349014282
BONKMILLON Unleashes Its Bonkers Potential on Solana.pdfcoingabbar
Introducing BONKMILLON - The Most Bonkers Meme Coin Yet
Let's be real for a second – the world of meme coins can feel like a bit of a circus at times. Every other day, there's a new token promising to take you "to the moon" or offering some groundbreaking utility that'll change the game forever. But how many of them actually deliver on that hype?
1. Elemental Economics - Introduction to mining.pdfNeal Brewster
After this first you should: Understand the nature of mining; have an awareness of the industry’s boundaries, corporate structure and size; appreciation the complex motivations and objectives of the industries’ various participants; know how mineral reserves are defined and estimated, and how they evolve over time.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the what'sapp information for my personal pi vendor.
+12349014282
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just what'sapp this number below. I sold about 3000 pi coins to him and he paid me immediately.
+12349014282
Can CEO compensation be justified, at least statistically?
1. 1. INTRODUCTION
Chief executive compensation is a highly debated subject amongst investors, politicians and
the average workers. The link between chief executive officer (CEO) compensation and
performance is important for investors, as it serves as a motivation to generate sustained
market related returns (Deysel & Kruger, 2015). The decisions of the board, are influenced
by the proportion of executives on the board and it is believed that this is the case for
compensation as well. Moreover, if the CEO is the chairperson of the board he/she may even
have more influence over the determination of their compensation package.
It is believed that a significant difference exist between the compensations of CEOs who
chair the board and those who do not, and this strongly prompts for in depth statistical
analyses of the assumed contributing variables. A strong correlation between compensation
and sales growth, and/or excess return is accepted as an indicator that compensation is
determined by performance. If a strong correlation exists between compensation and the
proportion of executives on the board, then it can be concluded that chief executives have
influence over the determination of their compensation. Other independent variables analyzed
are size of the board size and book value of the firm.
The aim of this research was to undertake statistical analyses, to determine the factors that
influence CEO compensation. The research work also aims to establish the extent to which
these variables can be used to predict CEO salaries.
2. METHODOLOGY
Data was collected from a sample of 300 medium and large UK businesses. The data
categories and definitions are;
Salary: CEO remuneration (salary, bonuses, etc.) £'s.
Dir: Number of directors on the board.
Exec: Number of executive directors on the board.
Assets: Book value of firms assets, £m. Measure of firm size.
Exec1: Proportion of executive directors on the board.
Dummy: 1 if the CEO and chairman of the board is the same person, 0 otherwise.
Sgrow: Sales growth, proportionate growth.
Exret: Excess return, proportion. Calculated as the return on companies’ shares on and above
the industry average (company return minus industry average). It is a measure of shareholder
wealth.
Lassets: Natural logarithm of firm assets figures.
2. 1
Lsalary: Natural logarithm of CEO remuneration figures.
All the data is annual. The natural logarithms of salary and assets have been included in order
to reduce the impact of outliers.
The analyses were performed in three phases. The first phase involved simple descriptive
statistics, in order to summarize and understand the sample. Descriptive univariate analyses
done were: mean, measure of dispersion (standard deviation) and measure of normality.
Skewness and kurtosis (peakdness) were used as a primary measure of normality in the
distributions, the variables with skewness and kurtosis greater than the modulus of the
doubled standard error, were considered to be asymmetrically distributed, and thus the
distributions are not normal (Tabachnick & Fidell, 2013). The variables that failed the
primary normality tests, were subjected to the Kolmogorov–Smirnov and the Shapiro–Wilk
tests for normality. A P value less than or equal to 0.05 was used to indicate 95% confidence
in the acceptance of normality.
The second phase involved bivariate analyses, to test if there is significant difference between
the salaries of the two groups (CEO is chairperson and CEO is not chairperson). The
independent variables were split and analyzed for normality. The Mann–Whitney U test was
used for non-parametric data and the Student's t-test for parametric data. The relationship
between salaries and all the other continuous independent variables (Dir, Exec, Assets, Exec
1, Sgrow, Exret, Lassets and Lsalary) was then tested in order to determine which ones
contribute in determining CEO salary. Scatterplots were formulated for depiction of
relationship and correlation tests for the establishment of the type (positive or negative) and
strength of relationship.
In the third phase, multiple linear regression was performed, for the sake of establishing
which of the variables can be used to extrapolate and predict CEO salaries. The variables that
did not show significant correlation were dropped, and the remaining ones were compared for
statistical differences after categorizing them in two groups namely, CEO is chairperson and
CEO is not chairperson. Multivariate analysis of variants (MANOVA) was performed to test
the differences between the means. This primarily tested if there is a difference in the
performance of companies, whose board is chaired by the CEO and those not, thus if a
difference exists in compensation of the two groups, it can be attributed to performance.
3. 2
3. RESULTS AND DISCUSSIONS
Phase 1: Univariate tests
The results in table 3.1 show that, only the number of directors, sales growth and excess
return follows the normal distribution according to the standard error rule. The dependent
variable, salary is significantly skewed to the right (+0.831), indicating that the sample
salaries are not normally distributed around the mean (£422 579.71), due to the fact that a
significant amount of CEOs from our sample, earn salaries that are far larger than the sample
mean.
The number of executives on the board as well as the proportion are also positively skewed,
+0.352 and +0.592 respectively. This means that some companies in our sample have too
many executives on the board than the normal. The average board in our sample consist of 15
directors, of which 4 are executives. This is also affirmed by the mean of the proportion of
executives on the board (~29%). The kurtosis values for number of executives on the board (-
0.664) and assets (-0.638) are lower than the minimum acceptance value in the normal range,
meaning that the distributions are too flat. This is also affirmed by the large standard
deviations relative to the mean. The natural logarithms of salaries and assets are also not
normal as the transformation of assets results into a highly peaked distribution (kurtosis =
15.912), meaning too many assets are now distributed in the centre, which is really just the
opposite of the untransformed data set. The natural log also transforms salaries into a
negatively skewed distribution (-0.318), which results in an opposing transformation of the
distribution. Although not exactly, it is nonetheless outside the acceptance range.
The values in red font are out of acceptance range.
Table 3.1
Descriptive Statistics
Variable
Mean
Std.
Deviation Skewness Kurtosis
Statistic Statistic Statistic
Std.
Error
Normal
range
(+/-) Statistic
Std.
Error
Normal
range
(+/-)
Salary 422579.71 218947.346 .831 .141 .281 .372 .281 0.561
Dir 14.63 2.793 .134 .141 .281 -.246 .281 0.561
Exec 4.06 2.237 .352 .141 .281 -.664 .281 0.561
Assets 7298.12 3840.009 .250 .141 .281 -.638 .281 0.561
Exec1 .2885 .17082 .595 .141 .281 -.219 .281 0.561
Sgrow .3548 .24625 .027 .141 .281 -.437 .281 0.561
Exret .2275 .11135 .032 .141 .281 .116 .281 0.561
Lassets 8.6628 .90543 -3.138 .141 .281 15.912 .281 0.561
Lsalary 12.8141 .54819 -.318 .141 .281 -.382 .281 0.561
4. 3
The Kolmogorov-Smirnov and Shapiro-Wilk test results in Table 3.2 confirms at 95%
confidence that most of the variables that failed the kurtosis and skewness normality tests are
not normal (P values less than 0.05). The natural log of salaries however, can be accepted as
normal as the P values (0.2 and 0.08) for both tests are larger than 0.05.
Table 3.2
Further tests of normality
Variable
Kolmogorov-Smirnov Shapiro-Wilk
P P
Salary .000 .000
Exec .000 .000
Assets .004 .001
Exec1 .000 .000
Lassets .000 .000
Lsalary .200 .008
5. 4
Phase 2: Bivariate tests
Phase 2 tests focussed on identifying if a significant difference exist between salaries of CEO
who serve as the chairperson of the board and those who do not. The normality tests results
between groups in Table 3.3, shows that the natural log of salaries, sales growth and excess
return are normally distributed.
Table 3.3
Tests of normality group wise
Dummy
Kolmogorov-Smirnov Shapiro-Wilk
P P
Salary 0 .002 .000
1 .000 .000
Exec 0 .000 .000
1 .000 .000
Assets 0 .186 .032
1 .093 .022
Exec1 0 .000 .000
1 .000 .000
Lassets 0 .000 .000
1 .000 .000
Lsalary 0 .200 .802
1 .200 .005
Sgrow 0 .200 .509
1 .200 .340
Dir 0 .024 .076
1 .000 .008
Exret 0 .200 .436
1 .200 .387
Table 3.4 shows that the mean salaries of CEOs who do not serve as chairpersons is £
281 149.70 and those who do earn a mean salary of £ 481 286.51. Out of the total sample, 88
companies have a different person as chairperson of the board, while the remaining 212
companies have the CEO as the chairperson of the board.
Table 3.4
Group Statistics
Dummy N Mean
Salary 0 88 281149.70
1 212 481286.51
6. 5
Comparison of means
In order to compare the salaries of the two groups, the Student's t-test for parametric data was
used to compare the natural log of salaries, as this distribution is normal. The Mann–Whitney
U test for non-parametric data was used for salaries, as the distribution is asymmetric and
unknown. The reason for doing the non-parametric test was to eliminate any doubt due to
logarithmic transformation.
The independent sample t-test results are shown in Table 3.4. The sub test, Lavene’s test for
equality of variances, shows that the variances of the two groups are equal (P larger than
0.05). This means that the dispersion of the natural logarithm of salaries are equal for the
groups. The P value for the t-test is less than 0.05, this implies that with 95% confidence,
there is a significant difference between the mean scores of salaries for CEOs who are
chairman, and those who are not.
Table 3.4
Independent samples t-test
Levene's
Test for
Equality of
Variances
t-test for Equality of Means
P t P (2-tailed)
Lsalary .803 -8.662 .000
In order to test if this difference is not by chance, the population effect size is calculated
(Cohen, 1992). This statistic uses the t value to calculate the difference between the two
groups. Eta squared is commonly used to calculate effect size. The equation is given as
(Pallant 2013, pp. 251-6);
Eta squared =
t2
t2+(N1+N2−2)
Where N1 and N2 are the sample sizes of the two respective groups. In this case we have 212
companies with CEO as chairperson and 88 where the chairperson is not the CEO, thus the
Eta squared value is 0.201. This implies that 20% of the variance in Lsalary is explained by
the fact that the CEO is the chairperson of the board. A value larger than 14% is considered a
large effect (Pallant, 2013).
A P value less than 0.05 was obtained for the Mann Whitney U test, which also implies that
there is a significant difference between the salaries of the two groups.
The preceding tests show that salaries of the two groups differ significantly, and that CEOs
who serve as chairpersons of the board earn more than those who do not. Scatterplots where
developed to illustrate the relationships between the independent variables and salaries.
7. 6
This was done in order to see which variables play a vital role in determining CEO salaries.
The scatterplots in the appendix illustrate that a stronger linear relationship exist between
salaries and performance (sales growth and excess return), than all the other variables. This
relationship is also positive.
Correlation tests
To further test the relationships of the variables with CEO salaries, correlation tests were
done to obtain the correlation coefficients (strength) and directions. Since the natural
logarithm of salaries follows a normal distribution, Pearson’s correlation test was chosen.
Table 3.5 shows the significant correlation coefficients obtained for the two groups. The
absolute strength is ranked as (Pallant 2013, pp. 139):
0.1 – 0.29: small
0.30 – 0.49: medium
0.50 – 1.0: large
Table 3.5
Pearson's correlation coefficients for salaries and independent variables
CEO is not Chairperson CEO is Chairperson
Exret .917** .904**
Sgrow .549** .637**
Dir -.235*
Lassets .148*
Exec .136*
**. Correlation is significant at the 0.01 level (99% confidence).
*. Correlation is significant at the 0.05 level (95% confidence).
A strong positive relationship is exhibited between salaries and performance (excess return
and sales growth) for both groups. There is no significant relationship between salaries and
size of the company or the number of executives on the board, for companies whose CEO is
not the chairperson. However, a small negative relationship is exhibited between salaries and
the size of the board. For companies whose board is chaired by the CEO, there is a small
positive relationship of salaries with size, and number of executives on the board. The
correlation coefficient squared gives what is known as the coefficient of determination, thus
excess return explains more than 80% of the variance in salaries. This is a significantly huge
contribution.
8. 7
Phase 3 Multivariate analyses
Regression tests
Regressions tests the ability of the variables to predict the CEO salaries. Multiple linear
regression is sensitive to outliers and multicollinearity between independent variables
(Fabozzi et al, 2013). Bivariate Pearson’s correlation tests between independent showed that
the number of executives on the board and the proportion has a correlation coefficient of
0.929. A cut of level of 0.7 was used (Pallant 2013, pp.164), thus there exist an overlap
between the two mention variables. The number of executives on the board was dropped
from the regression tests. Three outliers were detected and removed using the Mahalonobis
distance measure (Todeschinia et al, 2013).
An R squared value of 0.8662 was obtained, meaning the model explains 86.62% of the
variance in CEO salary. The significance of the model is verified by the accompanied P value
which is less than 0.05 (the null hypothesis is that R squared is zero, hence rejected).
Table 3.6 summarizes the unique contribution of each variable to the model. P values for
number of directors, sales growth and excess return are less than 0.05. This implies that only
these three variables make significant unique contributions to the model. The order of
strength in unique contribution is indicated by the standardized beta coefficients. The order
is: excess return (0.830), sales growth (0.137), number of directors on the board (-0.062).
The part correlation values indicate that excess return uniquely contributes the most to the R
squared (0.622^2 = 44%), followed by sales growth (0.105^2 = 1.1%) and number of
directors (-0.059^2 = 0.35%). Tolerance values less than 0.1 and VIF values above 10,
indicates the presence of multicollinearity. In this case there is none.
Table 3.6
Model Parameters
Model Unstandardized
Coefficients
Standardized
Coefficients
P Correlations Collinearity
Statistics
B Beta Part Tolerance VIF
(Constant) 11.673 .000
Dir -.012 -.062 .007 -.059 .889 1.125
Exec1 .019 .006 .804 .005 .838 1.193
Sgrow .306 .137 .000 .105 .585 1.708
Exret 4.091 .830 .000 .622 .561 1.782
Lassets .032 .041 .065 .040 .939 1.065
9. 8
The unstandardized coefficients in Table 3.6 were used to model CEO salaries as follows:
L (salaries) = 11.673 - 0.012 (Dir) + Sgrow (0.306) + 4.091 (Exret)
Where L is the natural logarithm, not to be confused with Log base 10.
The model is compared to the actual data as illustrated in Figure 3.1.
Figure 3.1
The strength of the predictability of the model, is confirmed by the curves in Figure 3.1.
The model closely resembles the actual data.
Multiple analysis of variants (MANOVA) test.
The independent sample t-test and the Mann-Whitney U test both showed that the salaries
of CEOs in the two groups differ significantly. It has also showed that CEOs who are
chairperson of the board earn more than those who are not. The correlation and regression
tests also showed that salaries of CEOs are determined by performance and the number of
directors on the board, furthermore the relationship between salary and performance is
positive and large, while that between the number of directors is negative and small. This
must mean that the salaries of CEOs who are chairpersons are high because they
outperform CEOs who are not chairpersons. The MANOVA test was used to see if there
is a significant difference in performance as well as the number of directors of the two
groups.
0
2
4
6
8
10
12
14
16
Lsalary
Lsalary (actual) vs Lsalary (modelled)
Lsalary
10. 9
The MANNOVA results are summarized in Table 3.7. The Lavene’s test for equality of
variances tests the null hypothesis that the variances are not equal. MANOVA tests
requires for variances between group variables to be equal. From table it can be seen that
the respective P values for the variables are above 0.05, hence we reject the null
hypothesis and accept that the variances are equal. The Wilks’ Lambda tests the null
hypothesis that there is a significant difference between the two groups (CEO is
chairperson and CEO is not chairperson), with respect to performance and number of
directors. The respective P value is less than 0.05, hence we accept the null hypothesis.
The Test between subjects effects, tests if the difference established by the Wilks’ Lambda
is due to all the variables. The respective null hypothesis is that the difference is due all
the variables. This null hypothesis is rejected because the P value for number of directors
(0.225) is larger than 0.05, therefore the only significant difference between the two
groups is performance (sales growth and excess returns).
Table 3.7
MANOVA summary
Levene's
Test of
Equality
of Error
Variances
Wilks'
Lambda
Tests of
Between-
Subjects
Effects
Effect
size
P P P Partial
Eta
Squared
Dir .308 .000 .255 .004
Sgrow .776 .000 .047
Exret .766 .000 .154
The proportion of variance is indicated by the Effect size Partial eta squared values. The
proportion of variance which is due to sales growth and excess returns are 4.7% and
15.4% respectively.
11. 10
4. CONCLUSION
Chief executives who are chairpersons of the board earn more than those who are not. The
difference in compensation is not due to the influence of the CEO on the board, due to
performance. The evidence can be seen in the relatively strong and positive correlation
coefficients obtained when comparing the relationships of salaries with each of the
independent variables. Excess return and sales growth show strongest correlation with
salaries. The number of directors produces a weak and negative correlation.
A model for predicting salaries was successfully developed. The model explains up to 87% of
the variance in CEO salaries. Only three variables produce significant unique contributions to
the model. In order, these variables and their unique contributions are: excess return (44%),
sales growth (1.1%) and number of directors (0.35%). The equation produced by the model
was plotted against the actual salaries from the sample and it was clearly seen that the model
mimics the actual curve.
The Multiple analysis of variants test (MANOVA) showed that the only significant difference
between companies with CEO as the chairperson of the board and those not, is performance.
Therefore it can be concluded that CEOs with more influence in the company, yield better
performance. In this research, performance was measured as excess shareholder return, above
the market average and sales growth proportion. It should not be a concern for investors to
invest in companies where the CEO the chairperson, as CEO remuneration is related to the
performance of the firm.
12. 11
5. REFERENCES
Cohen, J. 1992. Statistical power analysis. Current directions in psychological science. 1(3):
98-101.
Deysel, B., Kruger, J. 2015. The relationship between South African CEO compensation and
company performance in the banking industry. Southern African Business Review. 19(1).
137-169.
Fabozzi, J., Focardi, M., Rachev, T. 2014.Basics of Financial Econometrics: Tools,
Concepts, and Asset Management Applications. Somerset, US: Wiley.
George, D., Mallery, M. 2010. SPSS for Windows Step by Step: A Simple Guide and
Reference. 10th
edition), Boston: Pearson.
Tabachnick, B.G., Fidell, L.S. 2013. Using multivariate statistics, 6th
edition, Boston:
Pearson Education.
Todeschinia, R., Ballabioa, D., Consonnia, V., Sahigaraa, F., Filzmoserb P. 2013. Eta squared
and partial eta squared as measures of effect size in educational research. Analytica Chimica
Acta. 787:1–9
Pallant, J. 2013. SPSS Survival Manual: A step by step guide to data analyisis using IBM
SPSS, 5th
edition, McGraw-Hill
Xiaohong, H.C. 2012. Approaches to Quantitative Research, Cork, IE: Oak Tree Press