Norms prohibiting conflicts of interest apply in private fiduciary relationships and also to many public office holders. Whether or not such relationships are founded on trust, such norms can cultivate trust towards those holding governance authority, whether in interpersonal, civic or political relationships. In legal and philosophical discourse, however, conflicts of interest are rarely carefully defined. It has become a commonplace that ‘not every breach of duty by a fiduciary is a breach of fiduciary duty’. We must now go on to realize that ‘not every improper action by one who must avoid conflicts of interest is a conflict of interest’. A conflict of interest arises when duty-bound judgment is exercised in the presence of a conflicting interest. There can be conflicts that are not conflicts of interest; and there can be potential conflicts as well as actual ones. The simple misuse of power is not itself conflict of interest; on the contrary, it is the problem that the rules that forbid acting in a conflict situation aim to avoid. Bias is not identical with conflict of interest, although some kinds of bias arise from conflicts. Unauthorized profits are none of the above. They represent a different dimension of the relationship: not the supervision of powers, but the simple fact that when acting for another, one may not extract benefits for oneself. The rules about conflicts can foster trust in proper decision-making, while the rule against unauthorized profits can foster trust in the proper separation between a person’s private and official actions.

This chapter addresses the question of trust in fiduciary relationships, but more specifically the psychology of trust in fiduciary relationships. Unlike economics or sociology, disciplines that locate the notion of trust largely in terms of institutional or social facts, psychology asks about internal states. What are the cognitions and attributions that constitute trusting, on the one hand, or being regarded as worthy of trust, on the other?

Why you care: What is the point of running an experiment if you cannot analyze it in a trustworthy way? Variance is the core of experiment analysis. Almost all the key statistical concepts we have introduced are related to variance, such as statistical significance, p-value, power, and confidence interval. It is imperative to not only correctly estimate variance, but also to understand how to achieve variance reduction to gain sensitivity of the statistical hypothesis tests.

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.

This chapter introduces some terminology and themes that pervade the book. Compensation is defined broadly to include everything a worker likes about the job. “Strategic compensation” is about managing the compensation system to advance a specific organizational objective, typically profit maximization. The chapter discusses how this relates to talent management, turnover, retention, and employee productivity. Four recurring themes are introduced: (1) “Incentive effects” and “sorting effects (both of which affect the company’s labor productivity) arise when the compensation system is changed; (2) Market competition largely dictates pay levels, whereas employers have more control over pay design; (3) Competition forces employers to care about their employees’ preferences about pay; (4) Bargaining power also affects pay levels. The metaphor of a “3-legged stool” is introduced, in which compensation depends on workers’ desires, skills, and mobility. There’s discussion of what constitutes “fair” pay and the tradeoffs associated with allowing employees to know each other’s pay versus keeping compensation secret. The appendix offers a detailed treatment of nominal versus real compensation.

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).

While a versatile programming language such as Python can provide a framework to work with data and logic effectively, often we want to stay focused on data analysis. In other words, we could use a programming environment that is designed for handling data and is not concerned with programming so much. There are several such environments or packages available – SPSS, Stata, and Matlab. But nothing can beat R for a free, open-source, and yet a very powerful data analytics platform.

And just because R is free, do not think even for a second that it is somehow inferior. R can do it all – from simple math manipulations to advanced visualization. In fact, R has become one of the most-used tools in data science and not just because of its price.