Designing Pay level Argumentative Essay Example
Designing Pay level Argumentative Essay Example

Designing Pay level Argumentative Essay Example

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  • Pages: 16 (4395 words)
  • Published: December 22, 2017
  • Type: Essay
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Most organizations regularly adjust their employees' pay based on various factors. These adjustments can be influenced by market competition, performance, ability to pay, or contractual terms. It is common for employers to use market surveys to validate their job evaluation results. For example, if the evaluation places purchasing assistant jobs at the same level as secretarial jobs but the market shows significantly different pay rates for the two types of work, employers may reassess their evaluation process. Some employers may even establish separate pay structures for different types of work. IBM, for instance, sets pay based on market conditions for each occupation such as finance, engineering, or law. Hence, the job structure resulting from internal job evaluation may not align with competitors' external pay structures, leading to the need for reconciliation. Instead of integrating internal and external structures, some employe


rs directly rely on market surveys to establish their internal structures through "market pricing," which mirrors competitors' pay structures. Accurate market data becomes increasingly vital as organizations shift towards using more generic work descriptions that consider both the person's skill and the job itself.

The previous correlation between Job evaluation points and dollars may not be valid anymore. Accurate information and informed judgment are crucial for making these decisions. Competitive intelligence is obtained through survey data, which allows companies to understand how competitors achieve their market share and price their products/services. Companies compare their practices, costs, and compensation against competitors through benchmarking. The Employment Cost Index (ECI) is a publicly available source of labor cost data published by the Department of Labor. The ECI measures changes in employer costs for compensation and enables firms to

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compare their average costs to industry averages. However, industry averages may not accurately reflect relevant competitors in the market. Yahoo brings together individuals from various fields such as kindergarten teachers, software engineers, and sales representatives to collaborate on teams. Yahoo combines technology, media, and commerce in one company. Determining the relevant labor market and selecting which firms to include in Yahoo's surveys is important.Even in traditional companies, unique talent is needed for unique jobs. West Publishing, a provider of legal information to law firms, has created the role of Senior Director of Future Vision Services. The incumbent of this role is responsible for ensuring that West's customers (litigious lawyers) increase their online purchases and possess experience in e-commerce, marketing, and theater. It's challenging to find a job like that on the market.

These new organizations and jobs blend diverse knowledge and experience, resulting in markets that are more ambiguous than they are clear-cut. Organizations with distinctive jobs and structures face the dilemma of struggling to acquire memorable market data while simultaneously placing greater importance on external market data.

In most organizations, the responsibility for managing surveys falls upon the compensation manager. However, since compensation expenses greatly impact profitability, involving managers and employees in the task forces is wise.

To protect against potential lawsuits accusing "price-fixing," outside consulting firms are typically hired as a third-party safeguard. Some lawsuits claim that the direct exchange of survey data violates Section 1 of the Sherman Act, which forbids conspiracies that restrain trade.Participants in a survey may be accused of engaging in price fixing if the exchange of information has the overall effect of disrupting competitive pricing and artificially suppressing wages. It is

considered price fixing when participants' data is identified by the company name. The number of employers included in a survey does not have firm rules. Large firms with a leading policy may only exchange data with a few top-paying competitors, typically ranging from 6 to 10. For instance, Merrill Lynch aims to be at the 75th percentile among 11 peer financial firms. Conversely, a small organization located in an area dominated by two or three employers may choose to survey only smaller competitors. National surveys conducted by consulting firms often involve more than 100 employers. Clients of these consultants often request special analyses that provide pay rates categorized by selected industry groups, geographic regions, and/or pay levels (such as the top 10 percent). In the United States, the Bureau of Labor Statistics (BALLS) is the primary source of publicly available compensation data, excluding stock ownership. The BALLS publishes comprehensive information on various occupations (broadly defined as professional, executive, sales, and administrative support roles) in different geographical areas. According to the BALLS, administrative support in Birmingham, Alabama, pays $12.48 per hour in the private sector and $13.78 in the government sector.

In Iowa City, the rates are $12.38 and $17.98 for public and private sector employers respectively, with public sector employers in Iowa utilizing BALLS data more frequently. However, BALLS data alone is often not specific enough for private firms, who may use it as a supplementary check for other surveys. Tailoring analysis to industry segments, select companies, and job content is not feasible. In the past, comparing salaries was difficult and relied on word of mouth. Nowadays, employees can easily access data from BALLS, Salary.Com,

or other similar websites to compare their compensation. This accessibility means that managers must be prepared to explain and justify salaries compared to those found online. Whole Foods addressed this by openly sharing last year's pay information for all employees. However, the quality of salary data on the web is often questionable as it is mostly provided by site users. Additionally, some websites misuse the cost-of-living index for geographic salary comparisons. Conversely, provides a glossary of compensation terms and explains the sources and meaning of the statistics. When searching for programmer positions in Birmingham, Alabama, there are 37 job descriptions to choose from. Exhibit 8.5 displays the Salary.Mom results for three of those programmer jobs, both nationally and in Birmingham.

In contrast, the BALLS survey groups all programmer positions into one category, making it difficult to find a suitable match. There are numerous surveys available for compensation data, but few are validated. Opinions on the reliability of consultant surveys vary, with limited research conducted. The surveys by Hay, Mercer, Towers Perrine, Redford, and Clark Consulting can yield different results. Many companies use multiple surveys to determine pay levels for different job types.

Some employers combine the results of multiple surveys and assign weights based on their assessment of the data quality. However, there is no comprehensive study on the impact of various factors such as market definition, participating firms, data quality, and analysis methods on the results. Sample design and statistical inference are rarely taken into account.The performance of employment tests in staffing decisions is compared against a set of standards such as reliability and validity. Job evaluation's reliability and validity have been extensively studied and

debated. However, similar standards do not exist for market surveys and analysis. Without reliability and validity metrics, survey data can be challenged.

A general guideline for survey leveling is to select as few employers and jobs as necessary to achieve the purpose. The complexity of the survey decreases the likelihood of other employers participating. There are different approaches to selecting jobs for inclusion. Benchmark Jobs, which have stable job content, are common across different employers, and have a significant number of employees, can be chosen to represent the entire job structure in a survey aiming to price the entire structure. In Exhibit 8.6, benchmark jobs are indicated by the more heavily shaded jobs in the structures. These benchmark jobs are selected from various levels in each structure and matched with descriptions included in the survey. Exhibit 8 shows that approximately one in three organizations are able to match over 80 percent of jobs to salary survey jobs, while the remaining organizations report less success in matching.

The match between survey benchmark Jobs and each company's benchmark Jobs is assessed through different methods. The Hay Group, for instance, implements the same Job evaluation plan in multiple participating companies. As a result, comparisons can be made between Jobs in different organizations based on their Job evaluation points and the distribution of points among compensable factors. Some surveys simply ask participants to determine the degree of match. It is important for a good survey to include this information in its results.

According to a consultant friend, changes in Job matches occur when the compensation manager of a company changes as well. If an organization uses skill-competency-based structures or generic

Job descriptions, it may not have benchmark Jobs that can be matched with Jobs at competitors who use a traditional Job-based approach. Market data needs to be converted to fit the skill or competency structure. The easiest way to do this is by identifying the lowest- and highest-paid benchmark Jobs for the relevant skills in the relevant market and using the wages for these Jobs as anchors for the skill-based structures.

Work at various levels within the structure can then be slotted between these anchors. For example, if the entry market rate for operator A is $12 per hour and the rate for a team leader is $42 per hour, then the rate for operator B can fall between $12 and $42 per hour.

The effectiveness of this approach relies on how closely the extreme benchmark jobs align with the organization's work and if they truly encompass a wide range of skills. Attaching a pay system to two market data points increases the importance of the accuracy of those data.

In cases where an organization's job descriptions do not sufficiently match those in the salary survey, an attempt can be made to quantify the difference through benchmark conversion. If the organization utilizes job evaluation, they can apply their system to the survey jobs. By comparing the job evaluation points for internal and survey jobs, an estimate of their relative value can be determined and used as guidance for adjusting market data (although this decision is subjective).

Three main types of data are typically requested: information about the organization, information about the total compensation system, and specific pay data for each incumbent in the jobs being studied. The

provided Exhibit 8.8 outlines the necessary data elements and their inclusion logic. It should be noted that no survey includes all discussed data; the specifics depend on the survey's purpose and the jobs and skills being assessed.

Organization data is collected to highlight the similarities and differences among participating organizations in the survey.

Surveys of executive and upper-level positions include financial and reporting relationships data, as compensation for these roles is directly tied to the organization's financial performance. Typically, financial data is used to categorize firms by size, based on sales or revenues, rather than for analyzing competitors' performance. These data are used descriptively to report pay levels and mix by company size. The competitors' data has not been used for comparing productivity or labor costs.

However, there is a changing trend with the increased gathering of "competitive intelligence". This is causing a shift in the type of organization data collected and how it is utilized. Performance metrics such as turnover and revenues are now being collected. Surveys like Clark and Redford even collect turnover data. Other outcomes like earnings per share, market share, customer satisfaction, employee pay satisfaction, and various available sources (e.g. Google Financial, Thompson Financial) are also being considered.

Examples of these metrics include organization success indicators such as revenues, net income, and customer satisfaction, as well as turnover rates and recruiting data. To assess the total pay package and competitors' practices, information on all types of pay forms is required. Exhibit 8 provides a list of this information.

The text highlights the range of forms that can be included in each company's definition of total compensation. However, including too much detail on

benefits can make a survey burdensome. There are three commonly used measures of compensation: base pay, total cash (base, profit sharing, bonuses), and total compensation (total cash plus incentives and benefits). Exhibit 8.9 distinguishes between these alternatives and mentions their usefulness and limitations. Exhibit 8.0 displays results of a pay survey that includes these measures for engineers. Base pay signifies the cash amount assigned to each job and incumbent, while total cash includes base pay plus bonuses. Total compensation comprises of total cash, stock options, and benefits.

Total compensation, which includes the value of the employee's performance, experience, skills, and the value of the work itself, is higher than base pay alone or base plus bonus for all seven Jobs. The difference in compensation varies greatly, ranging from $7,842 (34 percent) for technician A to $244,103.38 (182 percent) for manager 3. On average, base pay only makes up 35 percent of total compensation for managers in this survey. Therefore, the measure of compensation is an important decision.

Misinterpreting competitors' pay practices can result in costly mistakes in pay levels and structures. It is important to verify the accuracy of job matches and look for anomalies, such as data that significantly deviate from others or outdated information. Additionally, factors like the nature of the organizations (e.g., industry, size) should be considered. Exhibit 8.11 is an excerpt from the survey used to prepare Exhibit 8.12, which was conducted for Fastest, a well-known small start-up.

Although there were multiple Jobs included in the survey, we will use information about engineer I as an example. Surveys may not be easy to read, but they provide a wealth of valuable


To access the information, proceed through the portal. The goal is to become the fastest analyst. Part A of the survey contains the job description. If the company's job is similar but not identical, some companies use the benchmark conversion/survey leveling method. This involves multiplying the survey data by a factor determined by the analyst to account for the difference between the company's job and the survey job.

Leveling is an example of judgment in survey analysis, leaving the decisions open to challenge. Part B of the survey displays actual salaries for engineer 1 positions. Analyzing the salary data helps identify any areas for additional consideration and provides insight into the data quality. For instance, in Part B of Exhibit 8.11, Company 1 has no engineer 1 employees receiving stock options, and five employees receive no bonuses. The bonuses range from $500 to $4,000. Given that there are 585 engineer Is in this survey, not all salary information is included. Individual-level data offers detailed information on specific practices, such as minimums, maximums, and the percentage of individuals receiving bonuses and/or options. Unfortunately, many surveys only provide summary information like company averages. Part C of Exhibit 8.11 presents company data. Here, it is important to first look for anomalies, such as one company dominating.If a company such as company 57 is included in the analysis, its data will be examined separately to understand its pay practices and their impact. It is unlikely that all employers will show similar patterns. For instance, in our survey, company 1 has a wide range of base pay for a single job, varying from $36,500 to $79,000. This suggests that

this company may use broad pay bands. While most companies have a bonus-to-base-pay ratio of about 2 to 3 percent, company 15 pays an average bonus of $8,254, resulting in a higher ratio of over 6 percent.

There are outliers in the data as well. For example, company 51 offers its engineers options valued at $74,453 in addition to their base pay. An analyst may consider excluding a company with this kind of unusual pay practice.

The question arises: what difference does it make if certain companies are excluded or included? To answer this question and understand anomalies better, it is advisable to conduct a separate analysis of these companies. These outliers may have intentionally set themselves apart by offering unique pay strategies. Examining competitors who differentiate themselves in this way can provide valuable insights. Additionally, combining the pay data of outliers with their financial information might reveal that the most successful competitors also provide larger bonuses to their engineers.

Part D at the bottom of Exhibit 8.1 presents summary data, including five different measures of base pay, cash, and total compensation, as well as the percentage of engineers who receive bonuses and options.The data indicates that most of Fascist's competitors provide bonuses but are less likely to offer options for this specific job. Summary data assists in condensing the survey information for further statistical analysis. Statistics assist Fastest in converting pages of raw data (Exhibit 8.11) into graphs displaying actual salaries (Exhibit 8.12), leading to a market line that reflects its competitive pay policy.

FREQUENCY DISTRIBUTION, often covered in basic statistics classes, can be more enjoyable on several websites. Our preferred website allows us to click anywhere

on a graph to observe how the addition of new data affects the regression line. To begin our analysis, examining the frequency distribution of pay rates is a helpful initial step. Exhibit 8.12 displays two frequency distributions derived from the data in the Exhibit 8.11 survey. The first distribution demonstrates the distribution of base wages for 585 engineers in increments of $1,000.

The second distribution reveals the total compensation for 719 engineers in increments of $10,000. Due to the wide range of dollar amounts, which includes figures below $90,000 and figures exceeding the highest values, many surveys employ logarithms for higher-level positions. Frequency distributions aid in visualizing information and may draw attention to anomalies. For instance, the base wage above $79,000 might be considered an outlier. Is this an individual with unique circumstances? Or is it an error in data reporting?A phone call (or e-mail) to the survey provider may provide an answer regarding the question about frequency distribution shapes. Unusual shapes in frequency distributions can indicate issues with job matches, widely dispersed pay rates, or employers with divergent pay policies. If the data appears reasonable, it could potentially be the result of two large errors that cancel each other out. Central tendency refers to a measure that condenses a large amount of data into a single number, as defined in Exhibit 8.13. It is crucial to differentiate between the "mean" and the "weighted mean." When only company averages are reported, the mean can be calculated by adding each company's base wage and dividing by the number of companies. However, using the mean might not accurately reflect labor market conditions since it treats the major employer's

base wage equally to the smallest employer's base wage. The weighted mean, on the other hand, calculates by adding all 585 engineers' base wages in the survey and dividing by 585 ($46,085). A weighted mean assigns equal importance to each individual employee's wage. Variation refers to how rates are spread out in the market and is indicated by the different patterns of variation shown in Exhibit 8.12's two frequency distributions.The standard deviation is a commonly used statistical measure of variation, although it is rarely used in salary surveys. In salary survey analysis, quartiles and percentiles are more commonly used. For example, someone may reference being in the 75th percentile nationally, which means that 75 percent of pay rates are at or below that point and 25 percent are above it. Quartiles, specifically the 25th and 75th percentiles, are often used to establish pay ranges.

Moving on to Exhibit 8.10, it displays the decisions made by the Fastest analyst in selecting salary survey jobs that closely match internal benchmark jobs. It also shows which companies to include and which measures of pay to use. Each compensation metric has a line connecting the pay for the seven jobs depicted on the horizontal axis. The jobs are ordered horizontally according to their position in the internal structure, based on the number of job evaluation points. This creates an upward trending line called the market pay line, which represents the market rates paid by competitors (obtained from the market survey) shown on the vertical axis.

The market pay line provides a summary of the distribution of going rates paid by competitors in the market. It can be drawn manually by connecting

the data points, as demonstrated in Exhibit 8.10, or statistical techniques like regression analysis can be employed.Regression is a method that creates a straight line to best fit data by reducing the variance around the line. In Exhibit 8.5, regression lines using pay survey data from Exhibit 8.10 as the dependent variable and Job evaluation points from matched Fastest Jobs as the independent variable are shown. Comparing the data tables in Exhibit 8.10 and Exhibit 8.15, Exhibit 8.0 displays market rates for survey Jobs, while Exhibit 8.15 shows the Job evaluation points for Fastest Jobs that correspond to these survey Jobs, along with the regression's "prediction" of each pay measure for each Job. For instance, the actual base pay for Tech A in the survey Job is $22,989 (Exhibit 8.10), while the "predicted" base pay for this Job is $23,058 (Exhibit 8.15). In Exhibit 8.16, we specifically examine the regression results that use base pay from the survey as the dependent variable, where diamond points represent the actual survey results and a solid line represents the regression result. Regression helps to smooth out large amounts of data while minimizing variations. As the number of Jobs included in the survey increases, the advantage of using a straight line provided by regression becomes apparent.

The market pay lines displayed in Exhibit 8.15 are particularly useful for determining salaries for Jobs that do not have a matching counterpart among Jobs included in the pay survey (i.e., nonobservance Jobs). For example, if we take a Job called Job Z, which has no match but has been assigned a Job evaluation points score of 110.One possible method of estimating the

base pay for Job Z is to use the information from Exhibit 8.15. Based on this exhibit, Fastest Job J has 100 Job evaluation points and matches a survey Job, Eng 5, with a base pay of $90,876. Using this information, we can calculate that the base pay for Job Z would be approximately $99,964 (110/100 X $90,876).

Alternatively, we can utilize the REGRESSION EQUATION from Exhibit 8.16 to estimate the base pay. According to this equation, the predicted base pay would be $98,412 when multiplied by 110 Job evaluation points and added to $15,522.56.

Although the results are not identical, they are very close. It is important to note that the regression line smooths the relationship between variables, resulting in a small difference in predicted base pay. Nonetheless, our market line remains highly valuable as it allows us to estimate the market pay for non-benchmark Jobs, even though only benchmark Jobs in our company can be directly matched to the survey.

Before concluding our analysis of survey data, it is essential to acknowledge that not all survey results will resemble our examples and not all companies employ these statistical and analytical techniques. There is no singular "right way" to analyze survey data. Our intention has been to provide insight into some useful calculations and assumptions underlying salary surveys.

As we approach the halfway point of this lengthy chapter, it might be a good time to consider utilizing Puking Pastilles, one of the Weasleys' Hazarding Wheezes described in the fifth Harry Potter book.

The Puking Pastilles cause a mild sickness that convinces professors to grant assignment extensions before the person magically recovers for some illicit time off. The relationship

between two parts of the pay model can be seen in Exhibit 8.17. The internally aligned structure is shown on the x-axis, consisting of Jobs A through P. Jobs B, F, G, H, J, M, and P are benchmark Jobs matched in the survey. Jobs A, C, D, E, L, K, N, and O do not have direct matching Jobs in the salary survey. The salaries paid by relevant competitors for those benchmark Jobs are shown on the vertical (Y) axis - this is the external competitive data. These two components - internal alignment and external competitiveness - come together in the pay structure, which includes the pay-policy line and pay ranges. If Colgate follows its claimed practice at the beginning of the chapter, it would use the 50th percentile for base pay and the 75th percentile for total compensation as measures in its regression. Referring back to Exhibit 8.14, the arrows on the right side of the exhibit illustrate how updating survey data reflects policy.

If the company chooses a "match" policy but updates survey data to the end of the current year/start of the plan year and maintains this rate throughout the plan year, it will be behind the market. It will only match the desired market pay level at the beginning of the plan year, while market rates continue to rise throughout the year. This is known as lead/lag, where market data is aged to a point halfway through the plan year. The original survey rates are updated to the end of the current year plus half the projected amount for the plan year ($48,431). Alternatively, an employer can choose to lead the

market by aging data to the end of the plan year ($49,612) and paying at that rate throughout the plan year.

Another approach to translating pay-level policy into practice is to specify a percentage above or below the regression line (market line) that an employer intends to match and then draw a new line at this higher (or lower) level. This pay-policy line would reflect a policy statement such as "We lead the market by 10 percent." Other options exist, such as including only a few top-paying competitors in the analysis and matching them ("pay among the leaders"), or leading for some job families and lagging for others. The key point is that there are various competitive pay policies available and different ways to implement them.If the company's actions do not align with its policies, employees will receive contradictory messages. Pay grades and ranges are used to accommodate external market pressures and differences among organizations. These differences include variations in the quality of individuals applying for work and their productivity or value. In addition to flexibility, organizations may also adjust rates for employees in the same job to recognize individual performance differences, meet employee expectations of pay increases over time, and encourage employee retention. Internally, pay ranges reflect an employer's desire to acknowledge differences in performance or experience with compensation.From an external competitiveness perspective, the range serves as a control device. It determines the maximum and minimum limits to what an employer is willing to pay for a particular job. Many organizations utilize a merit increase grid or salary increase matrix to determine pay raises. This matrix considers both employee performance rating and position within the

salary range. The objective is to continuously adjust employee pay to align with the market. Therefore, consistently high-performing employees should be compensated above the market median and range midpoint.

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