This project studies the impact of heterogeneous other-regarding preferences on many issues, such as taxation, government revenue and income inequality. The first two chapters build upon the literature of relative income effects pioneered by Duesenberry (1949). This hypothesis says that the utility of an individual depends on not only his absolute income level, but also his relative income position. An individual gains utility if his income exceeds the income of most members in his comparison group and loses utility if his income falls below the income of most members in the group. Most previous studies consider either a symmetric or a simple version of asymmetric case. In contrast, chapter 1 uses the British Household Panel Survey (BHPS) to depict a broader heterogeneity: income-dependent relative income effects. Results suggest that one's relative income effect indeed depends on one's income level. In chapter 2, we investigate the effect of these income-dependent relative income effects on an optimal income tax model. Simulation results show that the optimal tax system becomes more progressive to the extent that the relatively wealthy have stronger concerns regarding others' income than the relatively poor. This is an important result because it may provide theoretical evidence that increasing progressivity can be efficiency-enhancing. In chapter 3, we consider the effects of other-regarding preferences on equilibrium labor supply, tax revenue, and income inequality in a society where agents have heterogeneous skill levels and thus heterogeneous wage rates. Different from the previous two chapters, the model in this chapter is sufficiently flexible to allow the externality from agents who are "ahead" to differ in both magnitude and direction from those who are "behind." Our findings have important implications for tax policy.

This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

While the field of economics makes sharp distinctions and produces precise theory, the work of experimental economics sometimes appears blurred and may produce uncertain results. The contributors to this volume have provided brief notes describing specific experimental results.

Advances in the social sciences have emerged through a variety of research methods: field-based research, laboratory and field experiments, and agent-based models. However, which research method or approach is best suited to a particular inquiry is frequently debated and discussed. Working Together examines how different methods have promoted various theoretical developments related to collective action and the commons, and demonstrates the importance of cross-fertilization involving multimethod research across traditional boundaries. The authors look at why cross-fertilization is difficult to achieve, and they show ways to overcome these challenges through collaboration. The authors provide numerous examples of collaborative, multimethod research related to collective action and the commons. They examine the pros and cons of case studies, meta-analyses, large-N field research, experiments and modeling, and empirically grounded agent-based models, and they consider how these methods contribute to research on collective action for the management of natural resources. Using their findings, the authors outline a revised theory of collective action that includes three elements: individual decision making, microsituational conditions, and features of the broader social-ecological context. Acknowledging the academic incentives that influence and constrain how research is conducted, Working Together reworks the theory of collective action and offers practical solutions for researchers and students across a spectrum of disciplines.

Why do some people support redistributive policies such as a generous welfare state, social policy or protections for the poor, and others do not? The (often implicit) model behind much of comparative politics and political economy starts with redistribution preferences. These affect how individuals behave politically and their behavior in turn affects the strategies of political parties and the policies of governments. This book challenges some influential interpretations of the political consequences of inequality. Rueda and Stegmueller provide a novel explanation of how the demand for redistribution is the result of expected future income, the negative externalities of inequality, and the relationship between altruism and population heterogeneity. This innovative and timely volume will be of great interest to readers interested in the political causes and consequences of inequality.

The Agency for Healthcare Research and Quality (AHRQ) commissioned the RTI International–University of North Carolina at Chapel Hill (RTI-UNC) Evidence-based Practice Center (EPC) to explore how systematic review groups have dealt with clinical heterogeneity and to seek out best practices for addressing clinical heterogeneity in systematic reviews (SRs) and comparative effectiveness reviews (CERs). Such best practices, to the extent they exist, may enable AHRQ's EPCs to address critiques from patients, clinicians, policymakers, and other proponents of health care about the extent to which “average” estimates of the benefits and harms of health care interventions apply to individual patients or to small groups of patients sharing similar characteristics. Such users of reviews often assert that EPC reviews typically focus on broad populations and, as a result, often lack information relevant to patient subgroups that are of particular concern to them. More important, even when EPCs evaluate literature on homogeneous groups, there may be varying individual treatment for no apparent reason, indicating that average treatment effect does not point to the best treatment for any given individual. Thus, the health care community is looking for better ways to develop information that may foster better medical care at a “personal” or “individual” level. To address our charge for this methods project, the EPC set out to answer six key questions (KQ). Key questions for methods report on clinical heterogeneity include: 1. What is clinical heterogeneity? a. How has it been defined by various groups? b. How is it distinct from statistical heterogeneity? c. How does it fit with other issues that have been addressed by the AHRQ Methods Manual for CERs? 2. How have systematic reviews dealt with clinical heterogeneity in the key questions? a. What questions have been asked? b. How have they pre-identified population subgroups with common clinical characteristics that modify their intervention-outcome association? c. What are best practices in key questions and how these subgroups have been identified? 3. How have systematic reviews dealt with clinical heterogeneity in the review process? a. What do guidance documents of various systematic review groups recommend? b. How have EPCs handled clinical heterogeneity in their reviews? c. What are best practices in searching for and interpreting results for particular subgroups with common clinical characteristics that may modify their intervention-outcome association? 4. What are critiques in how systematic reviews handle clinical heterogeneity? a. What are critiques from specific reviews (peer and public) on how EPCs handled clinical heterogeneity? b. What general critiques (in the literature) have been made against how systematic reviews handle clinical heterogeneity? 5. What evidence is there to support how to best address clinical heterogeneity in a systematic review? 6. What questions should an EPC work group on clinical heterogeneity address? Heterogeneity (of any type) in EPC reviews is important because its appearance suggests that included studies differed on one or more dimensions such as patient demographics, study designs, coexisting conditions, or other factors. EPCs then need to clarify for clinical and other audiences, collectively referred to as stakeholders, what are the potential causes of the heterogeneity in their results. This will allow the stakeholders to understand whether and to what degree they can apply this information to their own patients or constituents. Of greatest importance for this project was clinical heterogeneity, which we define as the variation in study population characteristics, coexisting conditions, cointerventions, and outcomes evaluated across studies included in an SR or CER that may influence or modify the magnitude of the intervention measure of effect (e.g., odds ratio, risk ratio, risk difference).

We provide a systematic analysis of the properties of individual returns to wealth using twelve years of population data from Norway’s administrative tax records. We document a number of novel results. First, during our sample period individuals earn markedly different average returns on their financial assets (a standard deviation of 14%) and on their net worth (a standard deviation of 8%). Second, heterogeneity in returns does not arise merely from differences in the allocation of wealth between safe and risky assets: returns are heterogeneous even within asset classes. Third, returns are positively correlated with wealth: moving from the 10th to the 90th percentile of the financial wealth distribution increases the return by 3 percentage points - and by 17 percentage points when the same exercise is performed for the return to net worth. Fourth, wealth returns exhibit substantial persistence over time. We argue that while this persistence partly reflects stable differences in risk exposure and assets scale, it also reflects persistent heterogeneity in sophistication and financial information, as well as entrepreneurial talent. Finally, wealth returns are (mildly) correlated across generations. We discuss the implications of these findings for several strands of the wealth inequality debate.