So far in this book we have taken one topic or tool at a time and looked at how we could tackle a given data problem. Now, it is time to start bringing them together to develop a deeper understanding of the nature of data problems and methods, as well as extend our reach and skillset to address new problems that may emerge. There is, of course, no way we could cover all that you would encounter in real life, but we can certainly try to go through a few examples to see where you could take your data science skills.

This chapter covers compensating differentials, the theoretical foundation for most of the book. The key idea follows from Chapter 1's broad definition of compensation as “everything a worker likes about the job”. Jobs have many positive and negative characteristics, and workers vary in how much they value (or dislike) these characteristics. Positive job characteristics are a form of non-monetary pay, and negative characteristics diminish a worker’s effective pay. Holding other job characteristics constant, workers must be paid more to compensate them for a particular negative job characteristic and, similarly, are willing to accept less monetary pay when they enjoy a particular positive job characteristic. Workers sort across different jobs and employers based on their preferences for those job characteristics. The size of the wage differential (arising from a particular job characteristic) that occurs in the market is determined by the “marginal worker's” preferences for that job characteristic. Through a series of extensive examples, the reader is led to a thorough understanding of the marginal worker and compensating differentials, concepts which recur throughout the book.

Why you care: When running experiments, you also need to generate ideas to test, create, and validate metrics, and establish evidence to support broader conclusions. For these needs, there are techniques such as user experience research, focus groups, surveys, human evaluation, and observational studies that are useful to complement and augment a healthy A/B testing culture.

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.

This chapter describes the operational nationalization dataset in detail. The chapter begins by defining state-owned enterprises (SOEs) with de facto control of operations as a measure of operational nationalization. After describing how operational nationalization is measured, the chapter explains the coding and construction of the 187-country, 116-year dataset of national oil companies (NOCs) based on primary and secondary sources of each country’s petroleum history. Only 70 of these countries are major producers, but for completeness the full sample includes all sovereign countries with populations greater than 200,000 in 2000. This chapter includes several brief examples of NOC varieties, cases of NOC reforms and privatizations over time, as well as varieties of nationalization in nonoil sectors like copper, coal, zinc, cobalt, and lithium. The chapter also discusses how the database compares with existing nationalization datasets.

The chapter explains how and why the EU has intervened with both standards and procedural regulations in the case of organic agriculture, first in 1991 and again in 2007. The chapter begins with analyzing the development of private organic agriculture governance since the late-1960s. It shows how attempts at private governance harmonization, the expectation of EU intervention providing new productive opportunities for farmers, and active lobbying by the organic agriculture movement (especially IFOAM) resulted in the 1991 EU Organic Agriculture Regulation. The Regulation offered an organic production standard and modest procedural rules for private governance schemes. Continued problems due to a fragmented private governance market led the Commission to propose severe limitations on private schemes’ governance space in the early-2000s. Opposition to these proposals by private governance schemes, the organic movement, and key Member States prevented significant public intervention. Nonetheless, both standards and procedural regulations were strengthened in an updated Regulation in 2007 by the introduction of a mandatory EU organic logo and mandatory accreditation of private auditors.

This chapter coins the term “stakeholder fiduciary” to describe fiduciaries who are formal beneficiaries of their own exercise of fiduciary power. The fiduciary duty of loyalty applies differently to stakeholder fiduciaries, requiring not complete self-abnegation, but rather solidarity with other beneficiaries. Accordingly, a stakeholder fiduciary may retain an equitable share of the profits she generates through her position—even when such profits are the product of conflicted transactions or misappropriated opportunities. More striking still, when a stakeholder fiduciary participates in collective governance, she is entitled to vote exclusively in her own interests as long as she does not abuse her voting power to dominate other beneficiaries or undermine the purposes of the fiduciary relationship.

The chapter argues further that American courts give extra deference to the decisions of stakeholder fiduciaries. As long as a stakeholder fiduciary’s interests are plausibly consistent with the interests of other beneficiaries, courts will allow her to decide for herself how much time and energy she should devote to a particular decision. The best explanation for this deferential standard is that courts trust stakeholder fiduciaries to exercise due care without intrusive judicial oversight precisely because these fiduciaries have a direct stake in their own successful performance.

Why you care: Understanding the ethics of experiments is critical for everyone, from leadership to engineers to product managers to data scientists; all should be informed and mindful of the ethical considerations. Controlled experiments, whether in technology, anthropology, psychology, sociology, or medicine, are conducted on actual people. Here are questions and concerns to consider when determining when to seek expert counsel regarding the ethics of your experiments.

Python is a simple-to-use yet powerful scripting language that allows one to solve data problems of varying scale and complexity. It is also the most used tool in data science and most frequently listed in data science job postings as the requirement. Python is a very friendly and easy-to-learn language, making it ideal for the beginner. At the same time, it is very powerful and extensible, making it suitable for advanced data science needs.

Why you care: Running A/A tests is a critical part of establishing trust in an experimentation platform. The idea is so useful because the tests fail many times in practice, which leads to re-evaluating assumptions and identifying bugs.

Some judges and scholars hold that within legal limits and across legal frameworks, there is just a legal void, a domain in which law is absent. I challenge the legal void thesis, arguing that law operates within the spaces law creates. Law governs the interstitial spaces that exist within legal limits and across frameworks through its possession and assertion of legitimate authority. Importantly, its spatially seamless assertion of legitimate authority relies on a relationship of mutual trust between law-giver and legal subject. The argument begins by setting out the distinction between a decision-making entity’s authorization (ie, the process that led to it having authority) and its authority per se (ie, the nature of its legal power). The next section builds on the authorization/authority distinction and introduces the idea of mutual trust through the writings of Thomas Hobbes. Hobbes persistently uses the language of trust to characterize the position of the sovereign and other public officials. The second half of the paper sketches the conception of trust on which I rely, and explains how mutual trust informs law’s authority such that law can be understood to pervade the spaces it creates for the liberty of its subjects and officials

Trust matters to fiduciary law in a variety of ways. This chapter will focus on the importance of trust in advisory relationships, and it will emphasize two settings: categorical fiduciary relationships and ad hoc fiduciary relationships. In the former setting, I will suggest that these relationships are appropriately treated as fiduciary in part due to the likelihood of a beneficiary’s epistemic dependence on a fiduciary’s judgements. It is not necessary for epistemic dependence to exist in any particular advisory relationship to support this categorical treatment, so long as the likelihood of epistemic dependence is high enough across the category. In turn, the presence of trust supports the likelihood of that epistemic dependence. In the ad hoc fiduciary setting, I will suggest that these relationships are sometimes best seen as a kind of “involvement” (as that concept is developed in David Owens’s work). Involvements are voluntary relationships even though they may have no precise moment when they come into existence. Importantly, the existence of involvements is generally recognizable by the parties involved. Trust is relevant here as an aid in legally identifying such relationships.