The goal of this chapter is to introduce the reader to the wide range of methods used for performance analysis in healthcare. Specifically, it starts with a brief outline of what the authors refer to as the basic analytics of healthcare performance analysis, with an emphasis on hospitals. Although relatively simple, such basic performance analytics are popular approaches in practice: They are useful for exploring and presenting the data before proceeding with more sophisticated methods, and they provide a bridge for communication between practitioners and academics. To facilitate the discussion, they authors provide brief empirical illustrations using real data sets as well as supply the relevant R codes of the examples. They then briefly describe other major approaches for performance analysis in healthcare as a primer for the following chapters.

This chapter addresses the weaknesses of tax penalties in current law as deterrents of high-end tax noncompliance and describes how Congress could introduce tax penalties that vary depending upon taxpayers’ means. The chapter begins with a discussion of the possible motivations for individual tax compliance, including potential adverse consequences of noncompliance and, specifically, civil tax penalties. It then considers why current civil tax penalties often fail to deter high-end tax noncompliance. Finally, the chapter presents means-adjusted tax penalties as a new approach to the design of civil tax penalties, illustrates this approach with several examples, and addresses additional concerns.

The healthcare sector not only plays a key role in a country’s economy but is also one of the fastest growing sectors for most countries, resulting in rising expenditures. In turn, efficiency and productivity analyses of the healthcare industry have attracted attention from a wide variety of interested parties, including academics, hospital administrators, and policy makers. As a result, a large number of studies on efficiency and productivity in the healthcare industry have appeared over the past four decades in a variety of outlets. This chapter presents a performance analysis and science mapping of these studies with the aid of modern machine technology learning methods for bibliometric analysis. This approach revealed patterns and clusters in the data from 1,059 efficiency and productivity articles associated with the healthcare industry produced by nearly 2,300 authors and published in a multitude of Scopus-indexed academic journals from 1983 to 2021. Leveraging such biblioanalytics, which are combined with our own understanding of the field, the authors highlight the trends and possible future of studies on efficiency and productivity in healthcare.

The hospital industry in many countries is characterized by right-skewed distributions of hospitals’ sizes and varied ownership types, raising numerous questions about the performance of hospitals of different sizes and ownership types. In an era of aging populations and increasing healthcare costs, evaluating and understanding the consumption of resources to produce healthcare outcomes is increasingly important for policy discussions. This chapter discusses recent developments in the statistical and econometric literature on DEA and FDH estimators that can be used to examine hospitals’ technical efficiency and productivity. Use of these new results and methods is illustrated by revisiting the Burgess and Wilson hospital studies of the 1990s to estimate and make inference about the technical efficiency of US hospitals, make inferences about returns to scale and other model features, and test for differences among US hospitals across ownership types and size groups in the context of a rigorous, statistical paradigm that was unavailable to researchers until recently.

Conditional frontier models, including full and partial, robust frontiers, have evolved into an indispensable tool for exploring the impact of exogenous factors on the performance of the decision-making units in a fully nonparametric setup. Nonparametric conditional frontier models enable the handling of heterogeneity in a formal way, allowing explanation of the differences in the efficiency levels achieved by units operating under different external or environmental conditions. A thorough analysis of both full and robust time dependent conditional efficiency measures and of their corresponding estimators allows unravelling the compounded impact that exogenous factors may have on the production process. The nonparametric framework does not make assumptions on error distributions and production function forms and avoids misspecification problems when the data-generation process is unknown, as is common in applied studies. This chapter proposes a comprehensive review and journey through the conditional nonparametric frontier models developed so far in the efficiency literature. The authors show how this nonparametric dynamic framework is important for evaluating efficiency in the healthcare sector. They provide numerical illustrations on datasets from the Italian healthcare system, including summaries of practical implementation details.

This chapter provides basic production theoretical underpinnings used in modeling and measuring performance in the healthcare realm, which covers a host of institutions, practitioners, regulators, insurers, and patients, among others. This is a difficult task given the complexity of the healthcare sector. In general, the authors seek some meaningful benchmarks to use in assessing performance that are rich enough to model these complex entities. They begin with introducing technology or sets, which include as elements the many inputs used to treat patients in clinics or hospitals, which are in turn employed to improve patient outcomes (outputs), which may be multidimensional as well. They introduce key axioms that are imposed on these sets. However, although key in determining the benchmark or best practice possibilities, these sets are not practical for determining the performance of individual entities relative to the benchmark. More practical are functional representations of these multidimensional sets that are easy to estimate, which include distance functions and their dual value functions. These functions inherit properties from their respective technology sets, which in turn require certain specifications of their functional form if they are to be estimated parametrically.

This chapter describes two areas of legal theory that consider when means-based adjustments to legal rules may not be desirable. Under one perspective, means-based adjustments designed for redistributive purposes should be reserved for the tax system alone, since introducing means-based adjustments to other legal rules would entail greater efficiency costs. A second literature considers the desirability of a legal system that is impartial, nondiscriminatory, and general in its application. Subjecting taxpayers to different legal rules based on means could also undermine these important criteria. This chapter considers how means-based adjustments to the tax compliance rules should be evaluated from each of these perspectives, and why they would be justified even in cases where means-based adjustments to other legal rules would not be.

Economics is a central science to the understanding of regulation. Regulatory economics focuses on economic concepts that are relevant in regulatory contexts. Chapter 1 introduces key concepts of economics and regulatory economics, referring to a branch of social sciences concerned with how society chooses to employ its scarce resources to produce goods and services. This chapter offers a brief discussion of economic concepts that have shaped regulation (e.g., monopoly, market failures). It also discusses behavioral economics, the commons, and principal-agent theory.

Regulation has been given an unfairly bad name. As a top physicist who became an Energy Secretary and took an interest in fridges found out, the right regulation can do the opposite of what economists expect: accelerate innovation and cut costs, as well as cutting emissions.

Environmental violence focuses on the harm from both toxic and non-toxic pollution. Contemporary agricultural practices contribute significantly to such environmental violence through a heavy reliance on synthetic chemicals to raise commodities, as well as fossil fuels for tractors and shipping related to distribution. These same practices further harm persons and communities (both human and non-human) in ways not included in the environmental violence framework. Using Jacques Ellul’s theory of a technological society and technique that values efficiency in all areas of life, this chapter explores the relationship between environmental violence, contemporary agricultural practices, and wider critiques of technology. The chapter concludes with a brief examination of how we might culturally encourage an agriculture of flourishing rather than one focused solely on efficiency through the example of the Catholic Worker farms.

This chapter focuses on quantitative EEG processing, also known as EEG trend analysis. These types of algorithms process raw EEG data in a quantitative way, and display the data in more compressed forms that may be easier for the amateur EEG reader to interpret. Specific QEEG algorithms described in this chapter include amplitude integrated EEG, FFT spectrogram, rhythmicity spectrogram, asymmetry indices, and seizure detectors. Specific roles for QEEG covered include seizure detection and detection of focal cerebral ischemia.