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My purpose in this chapter is to address these issues and to determine how boards can do a better job of analyzing, understanding, and making decisions about how and how much people are paid. Later in the chapter we ll look at some of the underlying philosophical issues that result in how executives have been paid.
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THE TYRANNY OF COMPETITIVE DATA
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Corporate boards of directors, human resources departments, and compensation consultants have done a thorough job of analyzing competitive data and paying people according to that data. In fact they ve almost been religious about it. Their reliance on competitive data, however, has resulted in a kind of statistical tyranny, holding even well-intentioned boards captive to a standard methodology. Typically when boards make decisions about executive pay, they take several specific steps. Hire a compensation consultant to help analyze competitive pay practices and provide insight into what other companies are doing. In the vast majority of cases, the consultant is hired by management to advise both management and the board. Gather competitive data. Management participates in numerous competitive surveys that consist of hundreds of companies and thousands of positions, with data including salary, bonus, and long-term incentives. This information provides a tremendous amount of detail about compensation across a broad spectrum of companies. Analyze the data, according to industry groups or according to size. The information can be sorted in a variety of ways to find out, for example, what people in specific positions are paid in a particular industry or in a particular size and category of company. Compare compensation practices with those of peers and with the broader market of all companies. In particular companies want to see how they measure up compared with the median and the 75th percentile. With this comparison, a company can see where it ranks in
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CHAPTER SIX Providing the Right Questions and the Right Tools for Boards
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terms of salary, bonus, total cash compensation (salary and bonus), and long-term incentives (mainly stock options), and the entire total compensation package. The purpose of this whole process is to determine if the company is competitive in all areas of compensation and in line with its own compensation philosophy. For example a company may state it will pay at a certain level relative to a specific group of companies or type of company for salary, bonus, or long-term incentives, such as the median for salary, 60th percentile for bonus, and 75th percentile for long-term incentives. Admittedly this all sounds pretty logical. However there are three main problems with this approach. First of all no one wants to pay below the median, with a few exceptions. There is an inherent and obvious problem with the quest to be above average. As more and more companies try to pile on top of the median line, the level changes. Companies increase their compensation to pay at or above the median, which raises the median level. The companies that fall to the bottom half (and by definition half the companies must be below the median) then try to leapfrog above the median again. The second problem is how these comparisons are made. Typically, when a company is doing a competitive analysis of its compensation, it looks at data from companies with similar revenues. For example a $500 million steel fabricating company would look at data from manufacturing companies with revenues between $300 million and $1 billion. In addition a company may do a single regression analysis, looking at the statistical relationship between revenue and pay, to determine a more precise estimate of the right pay level for a company its size. While it all seems to make sense, there is something wrong with this picture. Companies are essentially paying based upon size and not performance. This is not because the people involved board members, compensation consultants, or whoever didn t know what they were doing. It s because this is how the data has been used. Back in the 1980s when I was at the consulting firm Towers Perrin, we tracked 30 to 40 different pieces of data related to compensation and ran sophisticated analyses to determine which data
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