3-DO= C_MBA_SALARY_FORECAST-EEOC_LABOR_CLASS_ACTION_LAWSUIT_DIVERSITY_AUDIT.doc

EMBA= SALARY FORECAST-EEOC LABOR & EMPLOYMENT DIVERSITY COMPLIANCE, CLASS ACTION LAWSUIT AUDIT = 211; MULITIVARIATE UNIVARIATE LINEAR & POLYNOMIAL REGRESSION FORECASTS OF MB= A’S SALARIES AS A FUNCTION OF BUSINESS SCHOOL RATINGS AND DIVERSITY.=

Abst= ract

This= study develops several diversity promoting MBA salary forecasting models. This is a model for EMBA (Executiv= e Masters in Business Administration), or MBA for short, salary forecast. We focus on one facet which is the= EEOC (Equal Employment Opportunity Commission) labor & employment diversity complia= nce. Or in a worst case scenario, in th= e case of incompliance and a resulting class action lawsuit we provide a forensic = audit tool that will help to correctly assess the litigation value for all the involved parties. This is sev= eral models including but not limited to the least square regression multivariate univariate linear & polynomial forecasts of business schools ranking &a= mp; MBA’S salaries and diversity. Our measure of diversity includes variables such as women students a= nd international faculty in the MBA programs of the employees. Disregarding the diversity in the = MBA programs themselves, the model helps in comparing the existing salaries to = the standard, finding the standard deviants as a warning, and verifying that th= ey are not the result of illegal discrimination. If they are a result of illegal discrimination, and the organization has not been sued yet, then correct it ASAP (As Soon As Possible) to avoid litigation. In contrast, and in the worse case= that a class action lawsuit is in progress, then the model can help calculate the amount of the discrimination, compensation, recovery and serve as an intern= al control to prevent it in the future.

INTR= ODUCTION

The = forecast of MBA salary will typically start with the hiring, firing, retaining employees, which are human resource functions. However, it should quickly s= pread into accounting, as the organization pays the salaries, and into the budget= ing and variance analysis as the organization evaluates the performance of its executive, many of which are MBA program alumni. After the involvement of the accou= nting and the economic activities, concerning the planning and implementation of labor and employment policies, the internal audit and legal function will h= ave to deal with compliance issues. The internal audit has to enforce compliance with US EEOC anti-discrimination, = or diversity regulations and we provide them with some salary forecasting mode= ls to assist in the compliance efforts and assess the risk and or damages due = to incompliance.

This= is a study of “What Makes MBAs Great?” and the answer is “Mone= y.” That is the salary of t= he alumni, and the corresponding contribution that they make to the cash inflo= w of the organizations that employ or hire them. Therefore, it becomes very importa= nt to be able to correctly forecast the MBA salary. We have forecasted MBA salaries us= ing a Multivariate Univariate Linear & Polynomial Regression analysis of MBA Ranking & Salaries. This study shows the symbiotic relationships of MBA program ranking and MBA salaries. It demonstrates how the salaries d= epend on other variables and the MBA media rankings, or MBA rankings for short. We build a forecasting simulator t= hat enables users to forecast the MBA salaries with about 90% accuracy that may= be very useful for litigators, auditor, forensic accountants and auditors that want to calculate the damages, or potential risk due to plaintiffs in US EE= OC (Equal Employment Opportunity Commission) anti-discrimination labor and employment= law violations. =

Univ= ersities and other organizations that want to estimate their future salaries or audit the past salaries, promotion, hiring and firing policies. Likewise, prospective students can= do the same, since most of the data is available publicly or can be easily int= erpolated and estimated.

Like= wise, we develop a model of forecasting the salary as a function of the rankings. This way prospective students can calculate their expected sala= ry after acquiring an MBA, to decide whether they can afford it or not, and how much to spend on an MBA program. Likewise, lenders can make better financing decisions based on MBA program potential salaries and ranking, evaluate better the default rate and risk associated with lending to MBA students to fund their education. Likewise, corporate sponsors of employees to earn an MBA, can assess the salary impact on their investment.= Employers that invest in an employee MBA education fundin= g, will forecast the salary and find what such employee is likely to bring back, ba= sed on their alumni salaries, and calculate their ROI (Return on Investment).

Deal= ing with Circular References of Cause and Affect Relationships of Variables based on Time Series Analysis

Thes= e mutual dependencies of rankings on salary and salary in turn dependency on ranking poses a methodological problem of variable role definitions. This circular reference may create some problems, so we suggest a cross-sectional time-series analysis that can resolve some of the problems. Such statistical and mathematical problems can be resolved over time. As we apply time-series in additio= n to cross sectional data, we eliminate some circular reference. In other words, if we use ranking = of the past 3 years to forecast the salary of the current year, we know that there= is no way the current salary, co= uld affect the rankings of previous years, due to the fact that these rankings preceded the salary. Therefor= e, in such a case, the current salary should be the dependant and predicted varia= ble, while the prior years rankings will be the independent predictors the affect the salary. This way we can illuminate some of the circular references, and their confounding effects. =

In t= he meantime, our objective is to review some issues and build some models, and test some hypotheses.

Revi= ew of the literature

SEX DISCRIMINATION CLASS ACTION CASES PROLIFERATE & GROW<= /p>

The proliferation of “Class Action Sex Discriminati= on” cases reported by the press raises the issue of preventative controls. This study looks at the case of AI= S faculty promotion from a forensic audit, expert witness testimony and computer litigation support point of view.

Class action sex discrimination suits have been discussed recently in the press. For instance Anthony S= ebok (2004) reported that the class action sex discrimination suit against Wal-M= art was certified a sex discrimination class action suit. When a= court "certifies" a class action, it allows the case to proceed with a class of plaintiffs, according to class action rules - as opposed to with a number of individual plaintiffs.) The class is claiming that Wal-Mart discriminated against women in the way it recruited and promoted managers.<= span style=3D'mso-spacerun:yes'>

A nationwide employment discrimination class action lawsu= it has been filed against Costco Wholesale Corporation (Patel, 2007).. The suit charges that Costco operates a "glass ceiling" at the store-management level which precludes women from obtaining promotion to assistant manager and general manager positions.

Wong (2008) reports about another similar case of gender discrimination lawsuit = against Smith Barney. The plaintiffs alleged that Smith Barney discriminated against female Financial Advisors in compensation and business opportunities. The parties entered into a four-ye= ar settlement agreement that is subject to Court approval.

This study uses the number of refereed publications as a measure of research productiv= ity. The authors develop a model to forecast the MBA salary as a function of the number of predictor independen= t variables. Likewise, this model of= fers a methodology of testing for diversity compliance by the significance of the = variance between the standard salary and its standard deviants. If the Sex is statistically signif= icant standard deviant (female MBA’s salary is way lower than the standard), then discrimination may exist in the population. In contrast, if Sex is statistical= ly insignificant, then discrimination is less likely in the population. If it appears like discrimination exists, then the plaintiff, the defendant, or a government agency may need = to set up a class-action server and let people join the class. We are developing such a server wi= th a survey to support such class-action lawsuits based on earlier work (Rushine= k, 1990, 1997, 2005).

The purpose of this study is to develop a regression anal= ysis connected to a continuous online survey that will raise red flags for the existence of sex discrimination. This is a tool for compliance forensic audit with regulations of government agencies, such as the EEOC in the United States.. The regression helps determine whe= ther sex is a significant factor in promotion decisions. If it is significant, then it may indicate that a class action may form at a future time, and enable the part= ies to reduce its likelihood of occurrence (Rushinek, 1993, 2000).

DIVERSITY INTERNAL CONTROLS FOR DISCRIMINATION PREVENTION=

We have reviewed numerous applications and sele= cted the best for evaluation of regulatory compliance such as the compliance with the US EEOC. What we are using here is a discriminant model,= a model that discriminates between significant and insignificant sex discrimination. Or, we can also call it diversity complian= ce and salary forecast. This is base= d on earlier studies by Rushinek and Rushinek (1985, 2000) that purports that t<= /span>he forensic auditor can provide effective expert witness testimony and computer litigation support regard“ Sex discrim= ination class action law suits. =

Quick= system response time is the main determinator of overall user satisfaction. (Rushinek, 1987, 1989). If fi= lling out the survey takes too much time, users will fail to fill it and the data will not be accurate. This st= udy deals with anti-discrimination compliance and MBA salary forecast software, it is an interactive online = expert system using diagnostic a= udit trails through telecommunication networks.= The audit trail is an important facet of the current study, due to t= he fact that we may need it to support the litigation.

MBA = MEDIA RATINGS CONSIDER DIVERSITY AND HELP FORECAST SALARY

Dive= rsity and compliance with anti-discrimination practices should start within organ= ization as well as MBA programs that educate executives to manage organizations. We are using media ranking statist= ics that deals extensively with such issues, as international and gender divers= ity in the MBA programs to develop our models.= The media is one of the most prominent ranking agencies of MBA progr= am, does it sell publications? Is= it a good predictor of Salary? We hypothesize that it is, and we prove it in the present study. We reject the null hypothesis that= it has no affect on salary, and it does not help forecast salary. Likewise, we propose to programmat= ically integrate such forecast into the ERP (Enterprise Resource Planning) systems= to correctly estimate the labor and employment cost of top MBAs, comply with t= he regulation, and avoid or effectively deal with litigations.

What= makes an EMBA (Executive Masters in Business Administration) great is money. This is the single most important = factor in a great EMBA program. We h= ave analyzed the data collected by the Financial Times (FT) business schools su= rvey which ranks the top 100 business schools EMBA (Executive Masters in Busines= s) programs, for the 2008 year, to decide what makes an EMBA program great as = FT suggests: http://rankings.ft.com/global-m= ba-rankings.

http://www.anderson.ucla.edu/x1= 6488.xml UCLA (University of California= at Los-Angeles) is proud to present their high rating. They publish an article entitled &= #8220;UCLA Anderson Ranked 10th nationally, 17th Internationally by Financial Times.” Likewise, Pen State http://www.smeal.psu.edu/news/latest-news/jan07/ftmba.html= reports t= hat “Smeal Ranks Among Financial Times' Top 25 U.S. MBA Programs UNIVERSI= TY PARK, PA (January 29, 2007) – For the second year in a row, the MBA Program at Penn State's Smeal College of Business ranks 25th domestically on the Financial Times' annual ranking of MBA providers.”

<= /p>

“AS MBA RANKINGS GROW M= ORE INFLUENTIAL, BUSINESS SCHOOLS ARE GOING TO GREAT LENGTHS TO BOOST THEIR SCO= RES. YET CONSIDERING HOW MUCH THE RESULTS VARY, STUDENTS ARE LEFT ASKING WHAT IT TAKES TO MAKE THE GRADE"

<= /p>

http://www.kellogg.northwestern.edu/news/hits/030301np.htm= reports that "Pulling rank: As MBA rankings grow more influential, business schools= are going to great lengths to boost their scores. Yet considering how much the results vary, students are left asking what it takes to make the grade.&quo= t; This is the focus of the current s= tudy; we aim to answer these questions. We explain why are these ranking so “influential,” and w= hy are “business schools are going to great lengths to boost their score= s?” The answers to both of these quest= ions are money. These ratings are = one of the most important predictor and determinant of the salary of the graduating MBA students, and thus the revenues of the schools and the universities. We develop a function that demonst= rates it statically, letting us quantify the exact impact. As “students are left asking= what it takes to make the grade?” We develop a simulating model that anybo= dy can easily predict what does it take within about 90% accuracy.<= /span>

<= /p>

Review of Prior Research=

A Business School is trying to design an impute control system, much like Rushinek, A. and Rushinek, S. de= sign an "Input Control of Accounting Information Systems," (September 1982), enter predictor variables, and output the forecasted ranking. Follow= ing the Business School as Rushinek, A. explains has to use "Capital Budge= ting Techniques, The Payback Period, The Net Present Value, The Internal Rate of Return and Their Computer Applications," (1983) to decide whether it is worth it. <= /b>

<= /p>
At this time the school has to develop a variance analysis, as Rushinek, A. describes "Control Aspects of Standard Costing: Variance an= alysis, Inflation Adjustment, The Learning and Their Computer Applications," (1983) set a system with standard goals, compare actual to standard, produce variances, learn the lesson, and repeat the cycle. In the current case an organizatio= n will forecast MBA employee’s salary, and use it to set up standard cost for employing an MBA from the top business schools. Based on this basis the employer c= an perform variance analysis, find the unfavorable variance, where they overpay the MBA, and make the proper adjustments, for example. Likewise, the employer may find th= at a favorable variance may be due to systematically discriminating against minorities (woman, foreign employees, etc) and correct the problem.

Then comes the cost accumulat= ion part as Rushinek, A. explains "Cost Accumulation for Different Costing Systems and Their Computer Applications," (1983) apply, select the best system. A system that can cop= e well with calculating the cost of the inputs such as hiring MBA or sponsoring and funding employee’s education in the top MBA programs. Lawyers, may be able to construct = better contracts when their company’s sponsors funding the education of employees that need to get an MBA from a top program. How many years should a soldier ha= ve to serve in order to get his MBA funded by the US army, navy or other military branches around the world? It depend on the salary that such degree will generate. =

<= /p>
Next the organizations should report the financial results, as Rushinek, A. suggests in "Costing Asp= ects of Segmented Reporting and Its Contribution Approach," (1983) produce = a forecasted contribution income statement to ensure that the revenues exceed the costs. In the event that the = costs are too high, as Rushinek, A. and Rushinek, S. suggest apply "Cost Con= trol Systems," (1983) make sure that the expenses do not exceed the cash available and the expected benefits. All of these forecasted reports have to be segmented by employee education, specifically, MBA and their forecasted salary, especially for the top MBA programs, that may be more expensive.

<= /p>
The investment model is next,= as Rushinek, A., Rushinek, S., Chang, L. and Most, K. imply we can use "T= he Construction of an International Investors' Perception Model for Corporate Published Forecasted Financial Reports for the United States, Great Britain, and New Zealand," (December 1983) even if an individual organization is not a corporation, in this case we should emulate a corporate environment, since = we are dealing with MBAs salaries, and they are sensitive to the MBA market pl= ace.

<= /p>
At this point we are ready to allocate resources as Rushinek, A. and Rushinek, S. explain in "Econom= ic Decisions and Resource Allocation in Managerial Accounting,"(March 198= 4) dedicate sufficient resources to hire MBAs. Likewise universities will hire faculty to teach MBAs, reduce teachi= ng load for more research time, etc. Schools should setup a Management Account= ing Information Systems to evaluate the process, as Rushinek, A. and Rushinek, = S. explain in "Management Accounting Information Systems," (1985), controlling the system through standard costs, and following their alumni salaries and careers. As Rush= inek, A. and Rushinek, S. explain use "Control Through Standard Costs and Variance Analysis," (February 1986), set up standards for new MBA employees, pay based on the forecasted salary, and then apply variance anal= ysis. To prevent discrimination, analyze= the variances to find whether the organization discriminates against minorities= or other protected categories, and illuminate such illegal discrimination. Nip it in the bud, before it becom= es a lawsuit, or worse a class action lawsuit.&= nbsp; However, if it is too late, and the organization is already defending itself against such a lawsuit, the organization can use a salary forecast m= odel to mitigate the deficiencies, and estimate the damages, and minimize the pe= nalties.

<= /p>
In the case of a large scale = class action lawsuit, the organization may have to setup a special fund raising a= nd spending actions as Rushinek, A. and Rushinek, S. explains in "Additio= nal Fund Allocation Constraints for Common Stock Investments: An Empirical Analysis of Regional Portfolios in the Common Market and the United States," (Spring 1986) = depending on the market, whether it is in the US or outside the US, for example. Likewise, we have to plan the solv= ency of such efforts as Rushinek, A. and Rushinek, S. describe in "Using Financial Ratios to Predict Insolvency," (1987) make sure that you do = not overspend and drive the school into insolvency. Much like a manufacture system, as Rushinek, A. and Rushinek, S. describe in "Financial Planning (FP) Case Study: Feature Selection Syst= em (FSS) for Microcomputer Users & Manufacturers," (October 1988) we = have to select the MBA employees that best fit the organizational needs. If it is a center to international business, then international diversity (faculty, students, board, languages, etc) should be promoted in the business, and the schools that it sponsors, = by funding employee education. <= o:p>

<= /p>
Much like an ERP (Enterprise Resource Planning) system, as Rushinek, A. and Rushinek, S. describe in "Manufacturing Resource Planning Systems (MPR) Case Study: Feature Selection System (FSS) for Microcomputer Users and Manufacturers", (19= 89) we have to apply it to a business labor and employment policy based on the labor marketplace outlook. If= it is a multi-national organization, like a multinational university, like Oxford= University, that has extension MBA programs, or multination companies, like IBM, that h= as subsidiaries in foreign countries, than transfer pricing applies. As Rushinek, A. and Rushinek, S. s= uggest "Multinational Transfer-Pricing Factors: Tax, Custom Duties, Antitrust/Dumping Legislation, Inflation, Interest, Competition, Profit/Dividend, and Financial Reporting", (1989) should be used in a variety of relevant countries. For this reason we have included multi-national MBA program in our sample. Accordingly, we should allocate the resources, as Rushinek, A. and Rushinek, S. describe in "Economic Decisions and Resource Allocation in Managerial Accounting", (August, 1989) match the resources to the expected revenues.
<= /p>

We are using rating and ranking of MBA programs much like Rushinek, A. and Rushinek, S. describe in "Rating and Ranking Best Practices of the British Petroleum Oil Company and the Oil & Gas Industry: Sales to Cash Forecasting Univariate Regression Trend Analysis and Computer Modeling, (December, 1997= ). We are dealing with the same construct but it can be with different industr= ies, oil and gas and industries other than Oil and Gas. We should avoid litigations and le= gal problems as we try to improve the diversity as soon as possible. As Rushinek, A. and Rushinek, S. e= xplain in "Litigation in Pennzoil and the Petroleum Refining Industry: A Financial Ratio Automated Peer Review Analysis of Variance Internet Software Development" (September,= 1998) compare the company to its peers to ensure compliance with regulations and standards. Sometime, we may use forensic accounting as Rushinek, A. and Rushinek, S. describe in "The Role of the Forensic Accountant in Calculating the Damages Using the 'But-if Analysis in a Case of Internet Day Trader & Online Broker Misconduct Litigation", (2001) avoid administrative misconduct as you try to improve the labor diversity before = it becomes a lawsuit. But if the company failed to do so, it is better later than never. Use a 'but-if' or an 'if then' ana= lysis to evaluate a variety of alternative, investigating potential risk of damag= es and recoveries. For example, = our salary forecast model can help us calculate what would have been had the organization stuck to standard salary practices calculate the amount of underpaid employee, and the damages of having to compensate them for their losses due to the discrimination against them. We are in the process of automatin= g such software systems that will provides an automated salary forecast, with an a= lert system that will provide red flags and warning of possible discrimination a= gainst certain employees. This way variance analysis will also deal with the threat of labor and employment litigations and quantify their associate risk. This is especially relevant for companies that tend to hire a lot of MBAs from top schools, and are concern= ed about diversity compliance.

Data= Inputs Collection and Analysis Automation Proposition

Inst= ead of using the tradition data collection survey methods, we propose to automate = the data collection and the analysis using new Microsoft Sharepoint technologies. This is a descr= iption of how data can be collected to continuously test for regulatory compliance, such as Sex discrimination compliance with the EEOC. The Microsoft ® SharePoint ® server will collect the data, regress Salary on the Rank, test the signific= ance and issue a warning as soon as it finds statistically significant Sex discrimination.

Scre= en Shot 1

Screen Shot 1 describes how to setup the survey online fo= r a lay-person (not an IT professional), such as a paralegal, a forensic audito= r etc. After selecting from a menu, the o= ption to display a survey the user will start picking up questions. In step 6, the user will pick multiple choice questions.&= nbsp; In our case, the system will apply to the role of the respondent to = the survey. Thus, the question wi= ll read as follows:

What= is your role?

1. Student

2. Faculty

3. Board member

4. Administrator or staff

5. None of the above

As t= he tip suggests “On the <Survey Name>: New Question page, in the Question box, type the question you want to ask, and then select the type of answer you want.”<= /p>

Scre= en Shot 2

Screen Shot 2 shows some of the advantages of an interact= ive online survey over a traditional survey, which is the branching capability. As the instructions suggest that branching lets you simplify yo= ur surveys for your respondents by displaying only questions that are relevant= to each respondent. Questions that are not relevant are not displayed. The relevance is determined by the respondent’s answer to the preceding question. For example, you mi= ght have the following questions in your survey:”

For = our survey, we will as the following:

1. Are you a US citizen? (choices: Yes or No)<= /o:p>

2. What national origin are you? (choices: Canada, = Europe, Asia, Middle east, Latin America, Others)

3. What was your salary prior to starting the MBA?<= o:p>

If a= survey respondent’s answer to question 1 is “No,” question 2 bec= omes irrelevant. Using branching, only respondents who choose “Yes” = for question 1 will be asked question 2. All others are taken directly to quest= ion 3.

Scre= en Shot 3

Screen Shot 3 shows another advantage of an online survey, the ability to preview the survey and find out if the survey developer made= mistakes, and correct them ASAP. Likewi= se, the user can create an e-mail message and include hyperlinks, as the instructions suggest: “After confirming that your survey operates as intended, send the survey. Rather than sending an e-mail message with a lin= k to the home page of a team site, you can send a URL (Universal Resource Locato= r) that takes survey recipients directly to the survey.” Entering the information directly = into the survey by-passes the problems with e-mail delivery that can be delayed = and misclassified as “junk mail,” never reaching its destination.   Thus, we “create an e-= mail message, and paste the survey address into the message. Your respondents wi= ll be able to click the link to go directly to the survey.”

Scre= en Shot 4

&= nbsp; Screen Shot 4 shows some instructions: “When drafting the e-mail message to survey recipients, be sure to include the following: 1) a concise descripti= on of what data the survey will collect, 2) an explanation of how this data wi= ll be used, 3) how long the survey will take to complete, and 4) whether the survey is anonymous.” I= n our case such a tip is very important since most people disregard the survey as= a waste of time and fail to respond, or fail to respond accurately. So it is important to explain the rationale for the survey as follows:

1) A= concise description of what data the survey will collect: this an EEOC anti-discrim= ination class action suit survey,

2) An explanation of how this data will be used: the data will be used exclusively for testing whether you are entitled to get paid due to illegal Sex discrim= ination against you (a respondent maybe more likely to respond knowing that it is n= ot just an “academic” survey,

3) H= ow long the survey will take to complete: this survey will take only 5 minutes to complete , and

4) W= hether the survey is anonymous: we need to get your information to audit the data,= and we need to verify it (most of it is available publicly) so it cannot be anonymous.

Another advantage is&nbs= p; the graphical summary of responses. An example of a graphical summar= y is shown which helps the respondents avoid mistakes as they can see graphically their responses and how it compares to the average of all other responses.<= span style=3D'mso-spacerun:yes'> So for example, when we ask for a = number of refereed journal articles, of top journals, a faculty member will not en= ter journal not listed on a hyperlinked “top journals” list, and overstate their publication rate unintentionally. Otherwise, we may have to remove o= utliers of respondents that are way out there.

Most important advantage is the instant and programmatic export to Microsoft Exc= el, which can calculate the regression analysis, and issue an alert based on predetermined conditions of deviations from the standard. Much like management by exception,= we can get an alert only in the case that the variance is significant, exceptional, and warrants additional rapid action.

Data Analysis Outputs

Over= all Predictive Quality of the Multivariate Linear Least Square Regression Analy= sis Model

SUMMARY OUTPUT

Table 1

Multiple R

R Square

Adjusted R Square

Standard Error

Observations=

ANOVA

Table 2

df

SS

MS

F

Significance F

Regression

Residual

Total

Salary inc. (%)

Recommends

Wom. students

Intl. faculty

Careers

Intl. course=

Intl. board<= /p>

Wom. faculty=

Value

Research

Placements

Intercept

Intl. mobility

Aims achieved

Intl. students

PhD faculty<= /p>

Wom. board

EMBA (Executive Masters in Business) programs, for t= he 2008 year. This survey ranking figures show the changes in the survey year to year, the pattern of cluster= ing among the schools is also significant. Some 185 points separate the top sch= ool from the school ranked 100 in the 2008 ranking. The top 15 schools, from the Wharton School of the University of Pennsylvania to the Tuck School of Busi= ness at Dartmouth College, form the leading group of world class business school= s. Some 63 points separate Wharton from Tuck. The second group is headed by Ya= le School of Management which scored 50 points more than the Tanaka Business School of Imperial College London, leader of the third group. The fourth gr= oup is headed by Eller College of Management of the University of Arizona and includes schools ranked from 57 to 100. Some 40 points separate these 44 schools.” Following is = an edited explanation and discussion of the questions in the survey, and the definitions of their calculations.

What= is not so clear is which variables are the most significant in this sample of MBA programs and= which of these variables is generalizable to the entire population of MBA program= s, for the purpose of forecasting the salary.

Gain in Pay That May Be Due To the Acquisition of The MBA

Salary today ($) is the average alumni salary three years after graduation. (= The 2008 ranking surveyed the MBA class that graduated in 2004). This figure includes alumni salary data for the current year and the one or two precedi= ng years, where available. The figure is NOT used in the ranking. We have included it in our model, = since it is a very significant factor in forecasting the ranking.

Weighted sal. ($) is the average alumni salary today with adjustment for salary variations between industry sectors. This figure includes data for the current year and the one or two preceding years where available. We have removed it from the model due to its correlation with other salary variables. Likewise, it takes= a bit of calculation to derive at this figure, which may complicate the user work= too much. We have excluded it fro= m the independent variables list since it is too dependent on Salary Inc (%).

Salary inc. (%) is the percentage increase in average alumni salary from before the MBA to today as a percent= age of the pre-MBA salary. This figure includes data for the current year and t= he one or two preceding years where available. In other words it is the Gain in p= ay that may be due to the acquisition of the MBA. This figure may be difficult to ca= lculate, but at the same time may be worthwhile for school to advertize, especially = if it is competitive. Such compe= titive gain in salary due to the MBA in a particular school may attract better students to such a school, raising its over-all performance. This is the single most statistica= lly significant variable in the entire list.

Value is calculated using the sa= lary earned by alumni today, course length, fees and other costs, including the opportunity cost of not working for the duration of the course. This turned out to be one of the insignificant variables that had a positive effect on the Ranking making th= em worse (higher score). It may represent excessively costly proposition that affects adversely the Ranking= . Likewise, this variable is statist= ically insignificant affect on salary. It is possible that it is somewhat too subjective.

<= o:p>

<= o:p>

C= areers is calculated according to changes in the level of seniority and the size of the company alumni are working in now versus befo= re their MBA. Data for the current year and the one or two preceding years are included where available. This is also an insignificant variable with an adverse effect on ranking. It may be due to excessive seniori= ty, which may be too close to senior citizens, and thus leading or reaching the point of diminishing returns.

<= o:p>

C= areers has also insignificant impact on Salary, it may be that company do not differentiate between alumni that remain with the company after getting the MBA, and new MBAs that did not work for the company prior to the MBA progra= m.

<= o:p>

<= o:p>

A= ims achieved the extent to which alumni fulfilled their goals or reasons for doing an MBA. This has been also an insignificant variable. It may be that it is somewhat more subjective and therefore the variance may have been too high = and too inconsistent.

Placements refer to alumni who used the careers service at their business sch= ool were asked to rank its effectiveness in their job search. This figure inclu= des data for the current year and the one or two preceding years where availabl= e. This is one of the insignificant variables, indicating lower reliance of placement services among students in the better schools. It may se= rve as a warning to schools that are trying to improve but are now way below the t= op schools to invest in placement services, since their students may need to r= ely on them more compared to the top schools.

Employment is the percentage of = the most recent graduating class that had found employment or accepted a job of= fer within three months of graduation. The figure in brackets is the percentage= of the class for which the school was able to provide employment data. We have excluded this variable fro= m the model due to low and insignificant contribution. Again, this may be due to the inconsistency of the data that school can provide about the employment of t= heir alumni.

Reco= mmends deals with where the alumni would recommend hiring new recruits. Alumni were asked= to name three business schools from which they would recruit MBA graduates. The ranking is calculated according to the number of votes for each school. Data for the current year and the one or two preceding years are included where available. Not surprisingly, = this variable was significant, we suspect that it is due to the subjective natur= e of such variable, but we are surprised by it negative effect. Why would it have a negative effec= t on salary, may be because the companies like to discourage nepotism, and like = to promote diversity. Or it is possible that since the employee like to work with their friends, they take= a hit in pay for the privilege to work with their old bodies.

=

Protected Categories for the Purpose of the US EEOC

The followi= ng categories are protected categories for the purpose of the US EEOC (United States Equal Employment Opportunity Commission). Therefore, they may be subject to anti-discrimination policies, and litigations, and especially class action lawsuits.

Since sex discrimination is ille= gal in the US, discriminating against women students, faculty and board members, may also be illegal, and may apply to the following category of variables.<= span style=3D'mso-spacerun:yes'> Wom. Faculty is the percentage of = female or women faculty. Wom. Studen= ts, and Board is the percentage of female students and board members, respectiv= ely. Therefore, for female faculty and board members= , for example, the statistical significance of this variable may indicate the existence of discrimination on the basis of sex regarding faculty, students, and board members, which is illegal in the US. On the other hand, it may also ind= icate the return to diversity or compliance with the law in terms of increasing t= he salary of MBA students and alumni due to the diversity.

Much= like sex discrimination, discriminating against students, faculty and board memb= ers because of their national origin may also be illegal. Therefore, the following variables= are interesting from an EEOC litigation support view-point. Intl. faculty, Students and Board = is the percentage of faculty, students and board members, whose citizenship differs from their country of employment. So if I am a student in the US and my citizenship is Israeli, I shou= ld be included in Int. student category. In general, this refers to foreign faculty, students, and board memb= ers, as opposed to their domestic counter parts. Therefore, for US faculty and board members, for example, the statistical significance of this variable may indicate the existence of discrimination on the basis of national origin regarding faculty, students, and board members. Likewise, it may indicate the retu= rn to diversity, in the current case, all these variables have a positive contribution to the salary. A= ll of these variables have a positive X Coefficient.

Intl. mobility is calculated according to whether alumni worked in different countries before the MBA, on graduation and also where they are employed to= day. This variable has an adverse effec= t on the ranking. It may be due to= the fact that these students could not find a job in the vicinity of their scho= ol and had to travel to foreign lands. It is also possible that the best schools provided more opportunities not forcing their graduate to migrate to remote locations as it relates to = the location of the schools or different countries.

Languages are the Number of extra languages required on completion of the MBA. Where a proportion of students require a further language due to an additional diploma, that figure is included in the calculations but not presented in the final table. PhD facu= lty shows whether these schools run additional courses for MBA students for whi= ch additional language skills are required. These figures are included in the calculations for the ranking but are not represented on the table to avoid confusion.   PhD rank is = the percentage of faculty with a doctoral degree. The language turns to be an insignificant variable and it may be due to the fact that English has become the facto business language. = So if a business man knows additional language it may be helpful, but not as significant as it used to be, when fewer business people spoke English.

&n= bsp;            = ;

Research is calculated according t= o the number of doctoral graduates from each business school during the past three years. Additional points are given if these doctoral graduates took up facu= lty positions at one of the top 50 full-time MBA schools of 2007. This is calculated according to the number of faculty publications in 40 international academic and practitioner journals. Points are awarded to the business school at which the author is currently employed. The total is weighted for faculty size.

Research turns to be insignificant= and have a negative effect on Salary. We have been surprised by this variable. However, academic and some of the practitioner journal articles that academic write are notorious for being irrelevant to the market-place, hence the saying that “this may be on= ly a moot academic debate” and therefore irrelevant for any practical purposes.     &nbs= p;

Another problem may be with the selection of only 40 journals, which does not represent properly the full s= cope of the research and focuses too much on the portion that may be excessively theoretical with too little practical impact on EMBA students. It is also possible the faculty indulgence in such research can be detrimental and at the expense of practi= cal degrees, such as EMBAs. =

Faculty members that focus too muc= h on such research, may not have the time and energy to deal with issues of teac= hing effectively and keeping up with the business practices. Therefore, their impact on the EMB= A may be marginal if not adversarial.   It is possible that these researchers lose touch with the business practices and have negative impact= on the salary of the alumni.

Char= t 1

Char= t 1- describes the Salary today ($) and the Residual Plot distribution about the horizontal access. It show that the residual distribu= te normally about the zero level of statistically

<= o:p>

A= ppendix 1 shows how chart 1 has been calculated for the set of the 1^st records. It shows that the residuals are about normally distributed and thus they comply with the regression assumptions of randomly distributed residuals, with a mean of ab= out zero.

<= o:p>

B= uilding A Univariate Regression Model to Forecast Salary

T= his time we forecast the Salary as a function of Media Rankings, in this case the FT rankings. We excluded it from= the multivariate model due to the insignificant contribution of the variable. It may be due to the dependency or= high correlations with all the other variables. At any rate it is significant a single variable in a univariate model, and therefore, it is important to consider it.= Much like Table 1, Table 1a shows how we are building a univariate regression model to forecast salary. Following is a logical symbolic description of the model, and its mo= re specific mathematical model using the actual variable names:

<= span style=3D'mso-tab-count:3'>        &= nbsp;           &nbs= p;

Y= _DepVar2=3Df(Constant+X_IndepVar_1)        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             &nb= sp;

S= alary today ($)=3Df(Intercept+3 yr. rank) &n= bsp;

T= able 1 a

SUMMARY OUTPUT

Table 1 a

Multiple R

R Square

Adjusted R Square

Standard Error

Observations=

ANOVA

Table 2 a

Regression

Residual

Total

X Variable 1=

Intercept

3 yr. rank

Chart 3 plots the Rank of the Resi= duals, or errors demonstrating the compliance with the linear regression assumptio= n of residuals that are randomly distributed around the average line of 0, zero errors, so in the long run they will average about zero.<= /p>

Chart 3

Chart 3 shows the Normal Probabili= ty distribution Plot, demonstrating compliance with the regression assumption = of a normal distribution. Such a distribution should appear about like half of a bell shaped curve, as is the case in the present model.

Building a Univariate Non-linear Regression Model and the Consistent Superiority of the Polynomial over the Linear Model

FORECASTING THE CURRENT SALARY AS A FUNCTION OF THE RANKING FOR 1 CURRENT PERIOD- A CROSS SECTIONAL ANALYSIS.

Our hypothesis is that as the Rank= ing goes down, or becomes better (with 1 being the top ranked MBA), the salary becomes larger. In other word= s, there is a negative mathematical relationship between the ranking and the salary.

This is a cross sectional analysis= , in which we are using one period of time, in our case year 2008 media rankings, data only. In the next analys= is, we will apply a time-series approach using multiple year data, to see whether = the same holds true over several years.

Chart 3a

Chart 3a demonstrates how we are building a univariate non-linear regression model and the consistent superiority of the polynomial over the linear model. Chart 3 shows both the univariate = linear and polynomial equation and R squares as follows:

Linear Model:

y =3D -598.1x + 14295

R² =3D 0.595, and its counter= part the

Polynomial Model:

y =3D 1E-05x6 - 0.003x5 + 0.392x4 - 21.17x3 + 560.5x2 - 7792.x + 17989

R² =3D 0.723.

The gain of about 20% in the R squ= are of the least squares method (regression method) illustrates the superiority of= the polynomial model over the linear model in predicting the 2008 ranking of the MBAs as a function of the Salary, of the top 100 programs.

Chart 4

FORECASTING THE CURRENT SALARY AS A FUNCTION OF THE RANKING FOR 3 LAST PERIODS- A CROSS SECTIONAL AND TIME-SERI= ES ANALYSIS.

Chart 4 demonstrates how we are forecasting the current salary as a function of the ranking for 3 last peri= ods- a cross sectional and time-series analysis. We are producing linear and polyno= mial models much like the previous cross-sectional analysis. Following are the functions and th= e R squares of the models respectively:

Linear Model:

y =3D -725.3x + 14608
R² =3D 0.599

Polynomial Model (of the 2^{nd<=
/sup>
degree):}

y =3D 14.05x² - 1891.x = + 16241
R² =3D 0.700

Chart 5

Linear Model:

y =3D -725.3x + 14608
R² =3D 0.599

Polynomial Model (of the 6^{th<=
/sup>
degree):}

y =3D 3E-05x⁶ - 0.006x<= sup>5 + 0.584x⁴ - 24.33x³ + 509.6x² - 6669.x + 1= 7865
R² =3D 0.745

The six degree produced a gain of = about .045 in the R square, raising it to .745 from about .7, as we can see in Ch= art 5.

While the linear model remains abo= ut the same comparing the cross sectional analysis of 100 top MBAs, the polynomial improves due to the addition of prior time-series data, from the previous time-series, for the top 82 schools (dropping 18=3D100-82 schools due to insufficient data). This show= s that the added years are more important than the lost universities due to incomp= lete data. So the conclusion is th= at whenever we can use both cross-sectional and time-series data if they are available, and even if we have to drop some observations, as long as enough observations remain.

For the future, we can build better models adding additional independent predictor variables to forecast the salary as a function of MBA ranking. This is of special s= ignificance for setting standard of salary and compliance with a variety of domestic and international regulations. Th= is is beyond the scope of the current study, but will definitely deserve some fur= ther attention.

Summary, Conclusions and Implicati= ons

In summary, we have built a multip= le linear regression model to forecast the salary of EMBA program alumni. Lawyers and auditors can use the m= odel to conduct a legal audit to see whether any discrimination and especially illegal discrimination exists. The forensic auditor can apply the function to all the employees with MBA to es= tablish a standard MBA salary. Furthe= rmore, they can see if there is a significant departure from that standard, especi= ally if this departure does not comply with the law. For example if the protected categ= ories of MBAs are paid significantly less the average of the industry and the com= pany compared to the rest of the employees, this may trigger an alert for possib= le illegal discrimination. Moreo= ver, if such lawsuits exist, then such models can be used by a forensic accounti= ng expert witness audit, and for computer litigation support and assessment of= the damages. We can calculate the= gap between the actual sub-standard salaries and the standard and estimate the damages due to the illegal discrimination.= These gaps will be the basis for calculating the damages and the compensations that the discriminating employer should pay to the plaintiffs= .

Business schools can use such a mo= del to assess their EMBA ranking prior to the official ranking agencies such as the Financial Times, AACSB, and others. We have found that the higher the s= alary of the alumni, the higher the media rank of the MBA program and the business school. Accordingly, business school decisi= on makers can make better decisions concerning planning, hiring, firing, budgeting, and retaining employees and faculty members. Likewise, students can use such a = model to assess the ranking of prospective enrollment in a certain EMBA program.<= o:p>

We have added salary forecasting m= odels in addition to the ranking forecasts. We have tested linear and non-linear least squares univariate and multivariate least squares regression analysis, as well as linear and polyn= omial models. All our models were s= tatistically significant, demonstrating the consistent superiority of the curvilinear mo= del compared to the linear model. We have regressed Salary on both independent variables, on a group of variable= s excluding media rankings, and as function of the media rankings.

In conclusion, our analysis shows = that some variables are statistically significant and thus we can reject the null hypothesis stating that their affect on ranking is zero or close to zero. At the .05 Level statistical significance we have rejected the null hypotheses with regard to some varia= ble Variables. We have rejected t= hese significant variables (X), in order of their statistical significance (P-value): Salary inc. (%), Recommends, Women Students, International Stude= nts, International Faculty. In contrast we have not rejected th= e null hypothesis with regard to other variables, which we included in the model, since they contributed to the overall R-Square. These Insignificant variables incl= ude: Research, Value, Careers, Internati= onal course, International board and Aims achieved. We have removed the rest of the variables from the model due to lowest significance, auto-correlation, dependence among independent variables and the lowest contribution to the overall R-Square.     <= /span>

The implications of our work inclu= de the direction of EMBA program to focus on the most significant variables. Students, their sponsors, lawyers, auditors, expert witnesses as well as school will look carefully at the financial gain that students will experience due to the enrollment in an EM= BA program.

Such issues may relate to the sala= ry increment (Salary inc. (%)), = and the current salary (Salary today ($)).&nbs= p; Likewise, alumni salaries will play a more important role in term of hiring faculty members, and promoting them to teach in MBA programs, over instructors that are not terminally qualified, instructors with only master= s’ degrees. Similarly, schools w= ill invest more in placement (Placements) centers that will help students find proper employment. With the globalization of trade international mobility of the graduating student will play a more important role in such MBA programs (Intl. mobility). Diversification will also play a m= ore important role in terms of female faculty members ( Wom. Faculty) as well as female board of director (Wom. Board) members as well as international stud= ents (Intl. students). Thus it see= ms like diversification policies, and compliance with anti-discrimination laws= in the US, including compliance with the EEOC, will continue to lead to affect ranking policies.

In the future, additional studies may examine more closely the relations between salary, gender and origin anti-discrimination compliance and class action employment and labor law lawsuits. Such studies may help determine th= e extent of such discrimination, and the damages that may result.<= /p>

We h= ave reviewed the some issues related to the negative and statistically signific= ant Wom. Faculty, Wom. Board, and Intl. students.   All of them describe protected categories by the US EEOC (United States Equal Employment Opportunity Commission) regulations. This may have far reaching impact on anti-discrimination compliance = and litigation, which are beyond the scope of the present study. In the future, it may be interesti= ng to develop a model to support such analysis of anti-discrimination forensic accounting expert witness testimony and computer litigation support. Such models will help forecast the salary of certain MBA graduates to alert the analyst and auditor to the lev= el of compliance with anti-discrimination policies. Likewise, this will assist in liti= gation support in cases of employment and labor discrimination and damage calculations.

In the future additional automated methodologies for diversity promotion and compliance should be incorporated into the organizational systems, and integrated into accounting and ERP. This will improve diversity and it= may also raise the salary of MBA and their productivity that must be linked to = the marketplace salary.

Char= t 1- Salary today ($) Residual Plot<= span style=3D'font-size:22.0pt;line-height:115%'>

Appendix 1

RESIDUAL OUTPUT

PROBABILITY OUTPUT<= /span>

Observation<= /i>

Predicted Rank '08<= /span>

Residuals

Standard Residuals<= /span>

Percentile

Rank '08=

FT.com business education: MBA and executive education rankings ... - 8:44pm

MBA ranking 2008. MBA 2006. The 100 top global MBAs, as compiled in this ranking. ...= "FT" and "Financial Times" are trademarks of T= he Financial Times Ltd. ...

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