This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.

This chapter covers a core problem that managers regularly face (i.e., negotiating with current or prospective employees over pay). The topic is usually omitted from compensation texts and is covered in separate courses in business programs. But negotiation over pay is such an integral part of strategic compensation and talent management that it cannot be omitted from a book that aims to train managers to think strategically about pay. For example, talent retention (Chapter 12) requires managers to respond correctly when employees receive outside offers from competitors, which immediately triggers bargaining and negotiation over pay. The chapter opens by stressing the importance of defining your objective. The most important ingredient to successful negotiation is information, so the questions of when and how to reveal and collect information are addressed in depth. Sections 14.4 and 14.5 examine threats and bluffs as negotiating tools, as well as how managers should think about and respond to counteroffers. As discussed in the final section, sometimes employers can gain the upper hand during bargaining by complicating the discussion, whereas other times simplification is better.

The chapter's premise is that understanding how something works requires studying it when it’s broken. The book is about labor contracts, i.e., formal or informal agreements between employers and employees. Sometimes employers breach these contracts by failing to pay their workers. Some workers (e.g., undocumented immigrants) are particularly vulnerable to “wage theft”. The timing of the parties' exchange of work and pay, and how it relates to wage theft, is discussed. Regulatory remedies to the wage-theft problem are studied, and it is shown that such regulations can lower workers’ average pay level by reducing the risk premium that compensates workers for wage-theft risk. Other remedies are given that involve no government intervention. Employers’ passive cuts to workers' real (as opposed to nominal) pay, via the erosive role of inflation, are discussed. Wage theft is offered as an example of a compensating differential (because it is an undesirable job attribute) before that topic is introduced. Themes from the wage-theft discussion recur throughout the book (e.g., in Chapter 10, on executive compensation, there is discussion of firms reneging on CEOs’ expected bonus payments).

In the previous chapter we were introduced to the concept of learning – both for humans and for machines. In either case, a primary way one learns is first knowing what is a correct outcome or label of a given data point or a behavior. As it happens, there are many situations when we have training examples with correct labels. In other words, we have data for which we know the correct outcome value. This set of data problems collectively fall under supervised learning.

Sherlock Holmes would have loved living in the twenty-first century. We are drenched in data, and so many of our problems (including a murder mystery) can be solved using large amounts of data existing at personal and societal levels.

These days it is fair to assume that most people are familiar with the term “data.” We see it everywhere. And if you have a cellphone, then chances are this is something you have encountered frequently. Assuming you are a “connected” person who has a smartphone, you probably have a data plan from your phone service provider.

This chapter teaches readers to think about training both as a form of current compensation and as an investment in future pay (because training makes workers more productive, allowing them to earn more in the future). Training is a form of current pay because workers value training (precisely because it increases their future expected compensation) and, for that reason, they are willing to accept lower current compensation than they would receive in an alternative job that is otherwise the same but that does not offer training. This evokes compensating differentials (Chapter 3). The portability of training across firms is covered, as well as whether employees or employers should pay for training and whether the skills imparted by training are general (i.e., useful across many employers) or specific to the current employer. The internal rate of return (or breakeven interest rate) is covered in the context of whether it is profitable to train workers. Section 8.5, on practical applications, gives tips for how managers can obtain information on the key components of the training decision, i.e., employee productivity and expected tenure after training, costs, and the interest rate.

This chapter on fringe benefits draws on the theoretical support structure of compensating differentials (Chapter 3), given that workers value fringe benefits (i.e., non-monetary components of pay) and are therefore willing to accept lower monetary pay than they would receive in alternative jobs that do not offer those benefits but that are otherwise identical. The chapter opens with a discussion of workers’ valuations of various fringe benefits and how those valuations may differ from the employers’ costs of providing those benefits. From a managerial standpoint, the main problem with using benefits to compensate workers is inefficiency, in that workers often value those benefits at less than their cash equivalents. Against that disadvantage are a number of advantages of paying workers in benefits, and the chapter covers the main ones. Cafeteria plans mitigate the main disadvantage of benefits compensation while simultaneously weakening some of the advantages. The chapter ends with a lengthy section on pensions that provides a detailed distinction between defined-contribution and defined-benefit plans and the implications for worker behavior (e.g., retirement ages).

This chapter treats pay in nonprofits and the public sector, where the organization’s objectives are not as straightforward as in the typical for-profit firm. It also covers small businesses, a subject which is neglected in standard compensation texts but which is important because some readers are or aspire to be small-business managers. The opening section defines the 3 entities under discussion. Organizational missions and workers’ intrinsic motivation are described, which relates to compensating differentials in that workers who value the organizational mission interpret it as a non-monetary component of pay that creates an incentive to work hard to further the mission. The chapter revisits external and internal constraints on pay, training (and recruitment of desired worker types), performance pay, and turnover, thereby tying the book’s earlier concepts together. Subjects that were covered in earlier chapters are re-examined through the different lenses of nonprofits, the public sector, and small businesses. The chapter ends with coverage of “distance” between managers and owners, which tends to be shorter in small businesses than in larger ones, and its implications for pay.

We started this book with a glimpse into data and data science. Then we spent the rest of the book, especially Parts II and III, learning various tools and techniques to solve data problems of different kinds. Our approach to all of this has been hands-on. And now we have come full circle. As we wrap up, it is important to take a look at where that data comes from, and how we should broadly think about analyzing it. This final chapter, therefore, is dedicated to those two goals, as you will see in the next two sections. One section is an overview of some of the most common methods for collecting/soliciting data, and the other provides information and ideas about how to approach a data analysis problem with broad methods. Then the final section provides a commentary on evaluation and experimentation.