Why you care: The choice of randomization unit is critical in experiment design, as it affects both the user experience as well as what metrics can be used in measuring the impact of an experiment. When building an experimentation system, you need to think through what options you want to make available. Understanding the options and the considerations to use when choosing amongst them will lead to improved experiment design and analysis.

This chapter presents a series of empirical analyses to test nationalization's primary effects on revenues and secondary effects on political survival. It begins by assessing the claim that nationalization will foster greater government take of resource revenues compared to maintaining operations by private firms. It then examines whether this corresponds to a higher probability of leadership survival: if nationalization increases state capture of resource revenues, then it should be the case that leaders use this wealth to consolidate power and prevent ouster. Beyond the survival of political leaders, it should also be true that political regimes in general will be stronger if resources are nationalized. These hypotheses are tested using the complete cross-national NOC dataset in conjunction with existing data on government revenues, the breakdown of regimes, and leadership survival. The empirics support the theory: nationalization increases state capture of resource revenues and increases the likelihood of survival of leaders and their political regimes. The results suggest that nationalizing operations explains why resource-rich leaders survive in some countries but not others.

This chapter covers internal constraints on pay, as opposed to the external constraints (namely labor law) covered in Chapter 4. Much is said about collective bargaining agreements in unionized settings, and the effect of unions on pay and pay dispersion. From the standpoint of managers, internal and external constraints are nearly identical in that both are sets of rules that must be followed to avoid negative consequences. One difference is that internal constraints are often more amenable to managerial influence; for example, collective bargaining agreements are renegotiated every few years, and management participates. The 3 Cs of compensation constraints are revisited in the context of internal constraints, as are compensation floor and ceilings. Pay compression is discussed, given its prevalence in unionized settings. Diverse preferences in the union membership are addressed in the context of a vote on seniority-based layoffs versus across-the-board temporary wage cuts, i.e., furloughs. Other (non-union) internal constraints are covered, such as those imposed on individual establishments by corporate headquarters, and company-wide design of the benefits package in pay plans.

Why you care: Statistics are fundamental to designing and analyzing experiments.

Why you care: Before you can run any experiments, you must have instrumentation in place to log what is happening to the users and the system (e.g., website, application). Moreover, every business should have a baseline understanding of how the system is performing and how users interact with it, which requires instrumentation. When running experiments, having rich data about what users saw, their interactions (e.g., clicks, hovers, and time-to-click), and system performance (e.g., latencies) is critical.

So far, we have seen data that comes in a file – whether it is in a table, a CSV, or an XML format. But text files (including CSV) are not the best way to store or transfer data when we are dealing with a large amount of them. We need something better – something that allows us not only to store data more effectively and efficiently, but also provides additional tools to process that data. That is where databases come in. There are several databases in use today, but MySQL tops them all in the free, open-source category. It is widely available and used, and thanks to its powerful Structured Query Language (SQL), it is also a comprehensive solution for data storage and processing.

This concluding chapter discusses the scholarly and policy implications of the book’s findings. Leaders that pursue predatory and opportunistic behavior are not as likely to fail as the conventional wisdom suggests; these leaders have little option to survive in power other than by seizing assets instead of building growth-enhancing institutions. This chapter then provides a policy roadmap to how extractive resources will be managed in the future for oil and for commodities that have thus far avoided nationalization. The examination of the possibility of future oil nationalizations in Lebanon and Guyana will be particularly relevant for states considering their ownership options in light of new discoveries. Rare-earth minerals and advanced materials involved in the production of renewable energy facilities and energy storage--namely cobalt, lithium, and palladium--have largely been produced by private firms. If and when the production of these materials is nationalized, it will have profound impacts not only on the leaders of producing countries, but also on the world that relies upon these resources to sustain the coming industrial revolution in clean energy.

This chapter provides more comprehensive coverage of promotions than is typically seen in compensation texts. The subject is important for compensation because employees' biggest raises usually involve promotions, so promotions are intimately connected to pay growth. Plus, promotion prospects are valued by workers and might make them willing to accept lower pay than they would receive in (otherwise identical) jobs that offer little or no promotion prospects, which connects to the concept of compensating differentials (Chapter 3). This chapter gets the reader-manager thinking about compensation structures within an entire organization, i.e., how the compensation differs across levels of the job hierarchy. The chapter opens by describing the role of promotions in creating worker incentives, both productive and perverse, and in matching workers to jobs ideally within the company. The question of why promotions usually come with big raises is covered, as is the important and common managerial problem of internal-versus-external hiring. The implications of turnover for promotions (and vice versa) are covered, as are up-or-out policies that require employers to fire non-promoted workers.

This chapter presents a detailed example that applies the compensation analytics concepts developed in Chapter 6. The reader is assumed to be a compensation consultant charged with evaluating whether gender-based discrimination in pay is present in a public university system in the sciences. Section 7.1 walks through the analysis step-by-step, from formulating the business question, to acquiring and cleaning data, to analyzing the data and interpreting the results from voluminous statistical output in light of the business question. Section 7.2 covers exploratory data mining, causality, and experiments. Exploratory data mining covers situations in which the manager does not know in advance which relationships in the data will be of interest, in contrast to the example in section 7.1 in which a statistical model and specific measures could be constructed that were directly tailored to address the business question at hand. Section 7.2 covers the challenges associated with establishing causality in compensation research and how experiments can sometimes be designed to address those challenges. Randomization and some pitfalls associated with compensation experiments are also covered

Why you care: You can run experiments either on a thin client, such as a web browser, or on a thick client, such as a native mobile app or a desktop client app. Changes for a webpage, regardless of whether it is frontend or backend, are fully controlled by the server. This is very different from a thick client. With an explosive growth of mobile usage, the number of experiments running on mobile apps has also grown (Xu and Chen 2016). Understanding the differences between thin and thick clients due to release process, infrastructure, and user behavior is useful to ensure trustworthy experiments.

Chapter four empirically assess the veracity of the argument on why leaders nationalize their oil sectors and establish operational national oil companies (NOCs). To test hypotheses derived from the theory’s implications, this chapter presents the findings from a method of statistical analysis that combines the cross-national NOC dataset with information elicited from structured interviews with oil experts. This technique -- Bayesian statistics -- allows for a holistic analysis of the determinants of nationalization that incorporates both quantitative and qualitative evidence on NOC formation. The results show the importance of information diffusion and perceptions of leader survival in the decision to nationalize the oil sector. The chapter then offers a case comparison of Iran and Saudi Arabia in the 1940s to show how information diffusion to a strong regime led to maintaining private ownership in Saudi Arabia in 1949, while knowledge about revenue sharing diffused to a weak regime led to nationalization in Iran in 1951.

The chapter explains how and why the EU has intervened with both standards and procedural regulations in the case of biofuels production, first in 2009 and again in 2015. The chapter begins with an analysis of the emergence of private biofuels governance since the early 2000s. It then discusses how in 2003 the EU established a policy stimulating the development of a domestic crop-based biofuels market based on the expected economic and environmental benefits of biofuels, while not directly addressing private governance. Once it became clear that the sustainability of biofuels needed to be regulated, the EU established standards and procedural regulations in the 2009 Renewable Energy Directive. Developing sustainability criteria was considered necessary to further support the economic opportunities of farmers and biofuels producers. The diversity of biofuel certification schemes also warranted EU-level control, with policymakers considering them useful instruments to verify compliance with public sustainability criteria in the form of a meta-standard approach. Continued problems with the diversity of private schemes resulted in additional procedural regulation in the 2015 ILUC Directive.