81-100 of 129 Results  for:

  • Health, Education, and Welfare Economics x
Clear all

Article

Incentives in Healthcare Payment Systems  

Ching-to Albert Ma and Henry Y. Mak

Health services providers receive payments mostly from private or public insurers rather than patients. Provider incentive problems arise because an insurer misses information about the provider and patients, and has imperfect control over the provider’s treatment, quality, and cost decisions. Different provider payment systems, such as prospective payment, capitation, cost reimbursement, fee-for-service, and value-based payment, generate different treatment quality and cost incentives. The important issue is that a payment system implements an efficient quality-cost outcome if and only if it makes the provider internalize the social benefits and costs of services. Thus, the internalization principle can be used to evaluate payment systems across different settings. The most common payment systems are prospective payment, which pays a fixed price for service rendered, and cost reimbursement, which pays according to costs of service rendered. In a setting where the provider chooses health service quality and cost reduction effort, prospective payment satisfies the internalization principle but cost reimbursement does not. The reason is that prospective payment forces the provider to be responsible for cost, but cost reimbursement relieves the provider of the cost responsibility. Beyond this simple setting, the provider may select patients based on patients’ cost heterogeneity. Then neither prospective payment nor cost reimbursement achieves efficient quality and cost incentives. A mixed system that combines prospective payment and cost reimbursement performs better than each of its components alone. In general, the provider’s preferences and available strategies determine if a payment system may achieve internalization. If the provider is altruistic toward patients, prospective payment can be adjusted to accommodate altruism when the provider’s degree of altruism is known to the insurer. However, when the degree of altruism is unknown, even a mixed system may fail the internalization principle. Also, the internalization principle fails under prospective payment when the provider can upcode patient diagnoses for more favorable prices. Cost reimbursement attenuates the upcoding incentive. Finally, when the provider can choose many qualities, either prospective payment and cost reimbursement should be combined with the insurer’s disclosure on quality and cost information to satisfy the internalization principle. When good healthcare quality is interpreted as a good match between patients and treatments, payment design is to promote good matches. The internalization principle now requires the provider to bear benefits and costs of diagnosis effort and treatment choice. A mixed system may deliver efficient matching incentives. Payment systems necessarily interact with other incentive mechanisms such as patients’ reactions against the provider’s quality choice and other providers’ competitive strategies. Payment systems then become part of organizational incentives.

Article

Information, Risk Aversion, and Healthcare Economics  

Hendrik Schmitz and Svenja Winkler

The terms information and risk aversion play central roles in healthcare economics. While risk aversion is among the main reasons for the existence of health insurance, information asymmetries between insured individual and insurance company potentially lead to moral hazard or adverse selection. This has implications for the optimal design of health insurance contracts, but whether there is indeed moral hazard or adverse selection is ultimately an empirical question. Recently, there was even a debate whether the opposite of adverse selection—advantageous selection—prevails. Private information on risk aversion might weigh out information asymmetries regarding risk type and lead to more insurance coverage of healthy individuals (instead of less insurance coverage in adverse selection). Information and risk preferences are important not only in health insurance but more generally in health economics. For instance, they affect health behavior and, consequently, health outcomes. The degree of risk aversion, the ability to perceive risks, and the availability of information about risks partly explain why some individuals engage in unhealthy behavior while others refrain from smoking, drinking, or the like. Information has several dimensions. Apart from information on one’s personal health status, risk preferences, or health risks, consumer information on provider quality or health insurance supply is central in the economics of healthcare. Even though healthcare systems are necessarily highly regulated throughout the world, all systems at least allow for some market elements. These typically include the possibility of consumer choice, for instance, regarding health insurance coverage or choice of medical provider. An important question is whether consumer choice elements work in the healthcare sector—that is, whether consumers actually make rational or optimal decisions—and whether more information can improve decision quality.

Article

Investments in Children’s Mental Health  

Daniel Eisenberg and Ramesh Raghavan

One of the most important unanswered questions for any society is how best to invest in children’s mental health. Childhood is a sensitive and opportune period in which to invest in programs and services that can mitigate a range of downstream risks for health and mental health conditions. Investing in such programs and services will require a shift from focusing not only on reducing deficits but also enhancing the child’s skills and other assets. Economic evaluation is crucial for determining which programs and services represent optimal investments. Several registries curate lists of programs with high evidence of effectiveness; many of these programs also have evidence of positive benefit-cost differentials, although the economic evidence is typically limited and uncertain. Even the programs with the strongest evidence are currently reaching only a small fraction of young people who would potentially benefit. Thus, it is important to understand and address factors that impede or facilitate the implementation of best practices. One example of a program that represents a promising investment is home visiting, in which health workers visit the homes of new parents to advise on parenting skills, child needs, and the home environment. Another example is social emotional learning programs delivered in schools, where children are taught to regulate emotions, manage behaviors, and enhance relationships with peers. Investing in these and other programs with a strong evidence base, and assuring their faithful implementation in practice settings, can produce improvements on a range of mental health, academic, and social outcomes for children, extending into their lives as adults.

Article

Labor Market Returns to Higher Education  

Ghazala Azmat and Jack Britton

The persistent high wage premium associated with college education, despite increasing participation rates, continues to generate a great deal of academic and policy interest. While it is widely agreed that the financial benefits associated with college completion outweigh the costs, modeling and empirically estimating the returns are complicated. A simple theoretical framework on educational investment illustrates the decision-making processes and key factors, such as expected returns, that guide the choice of an individual to engage in higher education and to achieve an optimal level of educational investment. Broadening the investment model, however, is instrumental to account for potential heterogeneous returns to higher education—the variation in returns by institution, field of study, and students’ background characteristics, among others—and to recognize the wider societal benefits of higher education, beyond private returns. The challenges involved in estimating the returns to higher education and the heterogeneity in returns are central in the discussion. Interpreting a naive correlation between education and wages is complicated by the nonrandom selection of individuals into higher education, such that individuals who are most likely to benefit from higher education are also those most likely to attend. Advancements in data collection, the ability to track individuals from compulsory education to the labor market, and improvements in econometric methodologies have enabled researchers to causally estimate the impact of higher education on earnings and allow for an improved insight into the disparities in returns to higher education. Recognizing the links between students’ characteristics (or backgrounds) and associated constraints helps to understand differences in higher education choices. Similarly, identifying differences in labor market returns associated with attending certain colleges or pursuing particular academic disciplines is as important in shedding light on the complex nature of human capital disparities and the signaling effect of higher education. As the costs of higher education provision constitute an increasingly large share of government spending all over the world, the high returns to college raise questions associated with who should pay for attending college and the role of the state. Internalizing the social returns to education and their broader implications on the growth and the persistence of inequality complicates this discussion. Higher education funding is one potential policy instrument to influence college attendance and returns. It is not, however, the only one. Better information on returns to education or access to policies that target members of certain social groups might be other potential tools to overcome constraints.

Article

The Lifetime Dynamics of Health and Wealth  

Pascal St-Amour

Life-cycle choices and outcomes over financial (e.g., savings, portfolio, work) and health-related variables (e.g., medical spending, habits, sickness, and mortality) are complex and intertwined. Indeed, labor/leisure choices can both affect and be conditioned by health outcomes, precautionary savings is determined by exposure to sickness and longevity risks, where the latter can both be altered through preventive medical and leisure decisions. Moreover, inevitable aging induces changes in the incentives and in the constraints for investing in one’s own health and saving resources for old age. Understanding these pathways poses numerous challenges for economic models. The life-cycle data is indicative of continuous declines in health statuses and associated increases in exposure to morbidity, medical expenses, and mortality risks, with accelerating post-retirement dynamics. Theory suggests that risk-averse and forward-looking agents should rely on available instruments to insure against these risks. Indeed, market- and state-provided health insurance (e.g., Medicare) cover curative medical expenses. High end-of-life home and nursing-home expenses can be hedged through privately or publicly provided (e.g., Medicaid) long-term care insurance. The risk of outliving one’s financial resources can be hedged through annuities. The risk of not living long enough can be insured through life insurance. In practice, however, the recourse to these hedging instruments remains less than predicted by theory. Slow-observed wealth drawdown after retirement is unexplained by bequest motives and suggests precautionary motives against health-related expenses. The excessive reliance on public pension (e.g., Social Security) and the post-retirement drop in consumption not related to work or health are both indicative of insufficient financial preparedness and run counter to consumption smoothing objectives. Moreover, the capacity to self-insure through preventive care and healthy habits is limited when aging is factored in. In conclusion, the observed health and financial life-cycle dynamics remain challenging for economic theory.

Article

Machine Learning in Policy Evaluation: New Tools for Causal Inference  

Noémi Kreif and Karla DiazOrdaz

While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, preoccupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the “fundamental problem of causal inference”) prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the unconfoundedness and positivity assumptions (also known as exchangeability and overlap assumptions). This article begins by reviewing popular supervised machine learning algorithms, including trees-based methods and the lasso, as well as ensembles, with a focus on the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g., the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g., targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates. Since there is no universal best estimator, whether parametric or data-adaptive, it is best practice to incorporate a semi-automated approach than can select the models best supported by the observed data, thus attenuating the reliance on subjective choices.

Article

Maternity Leave and Paternity Leave: Evidence on the Economic Impact of Legislative Changes in High-Income Countries  

Serena Canaan, Anne Sophie Lassen, Philip Rosenbaum, and Herdis Steingrimsdottir

Labor market policies for expecting and new mothers emerged at the turn of the 19th century. The main motivation for these policies was to ensure the health of mothers and their newborn children. With increased female labor market participation, the focus has gradually shifted to the effects that parental leave policies have on women’s labor market outcomes and gender equality. Proponents of extending parental leave rights for mothers in terms of duration, benefits, and job protection have argued that this will support mothers’ labor market attachment and allow them to take time off from work after childbirth and then safely return to their pre-birth jobs. Others have noted that extended maternity leave can work as a double-edged sword for mothers: If young women are likely to spend months, or even years, on leave, employers are likely to take that into consideration when hiring and promoting their employees. These policies may therefore end up adversely affecting women’s labor market outcomes. This has led to an increased focus on activating fathers to take parental leave, and in 2019, the European Parliament approved a directive requiring member states to ensure at least 2 months of earmarked paternity leave. The literature on parental leave has proliferated during the past two decades. The increased number of studies on the topic has brought forth some consistent findings. First, the introduction of short maternity leave is beneficial for both maternal and child health and for mothers’ labor market outcomes. Second, there appear to be negligible benefits from a leave extending beyond 6 months in terms of health outcomes and children’s long-term outcomes. Furthermore, longer leaves have little, or even adverse, influence on mothers’ labor market outcomes. However, evidence suggests that there may be underlying heterogeneous effects from extended leave among different socioeconomic groups. The literature on the effect of earmarked paternity leave indicates that these policies are effective in increasing fathers’ leave-taking and involvement in child care. However, the evidence on the influence of paternity leave on gender equality in the labor market remains scarce and is somewhat mixed. Finally, recent studies that focus on the effect of parental leave policies for firms find that in general, firms are able to compensate for lost labor when their employees go on leave. However, if firms face constraints when replacing employees, it could negatively influence their performance.

Article

Measuring Health Utility in Economics  

José Luis Pinto-Prades, Arthur Attema, and Fernando Ignacio Sánchez-Martínez

Quality-adjusted life years (QALYs) are one of the main health outcomes measures used to make health policy decisions. It is assumed that the objective of policymakers is to maximize QALYs. Since the QALY weighs life years according to their health-related quality of life, it is necessary to calculate those weights (also called utilities) in order to estimate the number of QALYs produced by a medical treatment. The methodology most commonly used to estimate utilities is to present standard gamble (SG) or time trade-off (TTO) questions to a representative sample of the general population. It is assumed that, in this way, utilities reflect public preferences. Two different assumptions should hold for utilities to be a valid representation of public preferences. One is that the standard (linear) QALY model has to be a good model of how subjects value health. The second is that subjects should have consistent preferences over health states. Based on the main assumptions of the popular linear QALY model, most of those assumptions do not hold. A modification of the linear model can be a tractable improvement. This suggests that utilities elicited under the assumption that the linear QALY model holds may be biased. In addition, the second assumption, namely that subjects have consistent preferences that are estimated by asking SG or TTO questions, does not seem to hold. Subjects are sensitive to features of the elicitation process (like the order of questions or the type of task) that should not matter in order to estimate utilities. The evidence suggests that questions (TTO, SG) that researchers ask members of the general population produce response patterns that do not agree with the assumption that subjects have well-defined preferences when researchers ask them to estimate the value of health states. Two approaches can deal with this problem. One is based on the assumption that subjects have true but biased preferences. True preferences can be recovered from biased ones. This approach is valid as long as the theory used to debias is correct. The second approach is based on the idea that preferences are imprecise. In practice, national bodies use utilities elicited using TTO or SG under the assumptions that the linear QALY model is a good enough representation of public preferences and that subjects’ responses to preference elicitation methods are coherent.

Article

Medical Malpractice Litigation  

David A. Hyman and Charles Silver

Medical malpractice is the best studied aspect of the civil justice system. But the subject is complicated, and there are heated disputes about basic facts. For example, are premium spikes driven by factors that are internal (i.e., number of claims, payout per claim, and damage costs) or external to the system? How large (or small) is the impact of a damages cap? Do caps have a bigger impact on the number of cases that are brought or the payment in the cases that remain? Do blockbuster verdicts cause defendants to settle cases for more than they are worth? Do caps attract physicians? Do caps reduce healthcare spending—and by how much? How much does it cost to resolve the high percentage of cases in which no damages are recovered? What is the comparative impact of a cap on noneconomic damages versus a cap on total damages? Other disputes involve normative questions. Is there too much med mal litigation or not enough? Are damage caps fair? Is the real problem bad doctors or predatory lawyers—or some combination of both? This article summarizes the empirical research on the performance of the med mal system, and highlights some areas for future research.

Article

Mismatch in Higher Education  

Gill Wyness

The first studies of higher education mismatch were motivated by a desire to understand the consequences of affirmative action policies, which lowered academic admission requirements for underrepresented students (typically disadvantaged racial and ethnic groups). This is the so-called “mismatch hypothesis,” which suggests that affirmative action may actually be harmful because it enables students to attend colleges they are academically underprepared for (“mismatched” to) while squeezing out students who would otherwise have enrolled and succeeded. At its heart, the study of mismatch is motivated by the proposed existence of complementarities between students and courses—the assumption that the highest-achieving students would get the most benefit from attending the highest-quality schools, and vice versa. Both undermatch—where high-attaining students attend low-quality universities—and overmatch—where low-attaining students attend high-quality universities—have been studied. Only a very small number of studies have been able to causally examine the impact of mismatch. A major challenge is that unobserved factors that influence individuals’ decisions to attend a particular college (and for the college to accept them) are likely to affect their likelihood of completion and their probability of doing well in the labor market. Several recent studies have made progress in this area, but the evidence on the impact of mismatch still shows mixed results, suggesting that more research is needed, for example, in studying different policy shocks (e.g., natural experiments such as the use of affirmative action bans, which create exogenous variation in mismatch) for students at different margins. There is also a need to expand the study of mismatch beyond the United Kingdom and the United States, which has been the main focus of studies so far, and also beyond higher education into other contexts such as further education colleges.

Article

Missing Women: A Review of Underlying Causes and Policy Responses  

Aparajita Dasgupta and Anisha Sharma

One of the most egregious manifestations of gender bias is the phenomenon of “missing women.” The number of missing women is projected to increase to 150 million by 2035, as a result of prenatal sex selection and excess female mortality relative to men, and is reflected in male-biased sex ratios at all ages. The economics literature identifies several proximate causes of the deficit of females, including the widespread use of prenatal sex selection in many Asian countries, which has been fueled by the diffusion of ultrasound and other fetal sex-detection technology. The use of prenatal sex selection has become even more expansive with a decline in fertility, as parents with a preference for sons are less likely to achieve their desired sex composition of children at lower levels of fertility. Gender discrimination in investments in health and nutrition also leads to excess female mortality among children through multiple channels. The deeper causes of son preference lie in the socioeconomic and cultural norms embedded in patriarchal societies, and recent literature in economics seeks to quantify the impact of these norms and customs on the sex ratio. Particularly important are the norms of patrilineality, in which property and assets are passed through the male line, and patrilocality, in which elderly parents coreside with their sons, whereas their daughters move to live with their husbands’ families after marriage. Another strand of the literature explores the hypothesis that the devaluing of women has roots in historical agricultural systems: Societies that have made little use of women’s labor are today the ones with the largest female deficits. Finally, economic development is often associated with a decline in son preference, but, in practice, many correlates of development, such as women’s education, income, and work status, have little impact on the sex ratio unless accompanied by more extensive social transformations. A number of policies have been implemented by governments throughout the world to tackle this issue, including legislative bans on different forms of gender discrimination, financial incentives for families to compensate them for the perceived additional costs of having a daughter, and media and advocacy campaigns that seek to increase the inherent demand for daughters by shifting the norm of son preference. Quantitative evaluations of some of these policies find mixed results. Where policies are unable to address the root causes of son preference, they often simply deflect discrimination from the targeted margin to another margin, and in some cases, they even fail in their core objectives. On the other hand, the expansion of social safety nets has had a considerable impact in reducing the reliance of parents on their sons. Similarly, media and advocacy campaigns that aim to increase the perceived value of women have also shown promise, even if their progress appears slow. Analysis of the welfare consequences of such interventions suggests that governments must pay close attention to underlying sociocultural norms when designing policy.

Article

Modeling Chronic Diseases in Relation to Risk Factors  

Pieter van Baal and Hendriek Boshuizen

In most countries, non-communicable diseases have taken over infectious diseases as the most important causes of death. Many non-communicable diseases that were previously lethal diseases have become chronic, and this has changed the healthcare landscape in terms of treatment and prevention options. Currently, a large part of healthcare spending is targeted at curing and caring for the elderly, who have multiple chronic diseases. In this context prevention plays an important role, as there are many risk factors amenable to prevention policies that are related to multiple chronic diseases. This article discusses the use of simulation modeling to better understand the relations between chronic diseases and their risk factors with the aim to inform health policy. Simulation modeling sheds light on important policy questions related to population aging and priority setting. The focus is on the modeling of multiple chronic diseases in the general population and how to consistently model the relations between chronic diseases and their risk factors by combining various data sources. Methodological issues in chronic disease modeling and how these relate to the availability of data are discussed. Here, a distinction is made between (a) issues related to the construction of the epidemiological simulation model and (b) issues related to linking outcomes of the epidemiological simulation model to economic relevant outcomes such as quality of life, healthcare spending and labor market participation. Based on this distinction, several simulation models are discussed that link risk factors to multiple chronic diseases in order to explore how these issues are handled in practice. Recommendations for future research are provided.

Article

Models of Health and Addiction  

Audrey Laporte and Brian S. Ferguson

One of the implications of the human capital literature of the 1960s was that a great many decisions individuals make that have consequences not just for the point in time when the decision is being made but also for the future can be thought of as involving investments in certain types of capital. In health economics, this led Michael Grossman to propose the concept of health capital, which refers not just to the individual’s illness status at any point in time, but to the more fundamental factors that affect the likelihood that she will be ill at any point in her life and also affect her life expectancy at each age. In Grossman’s model, an individual purchased health-related commodities that act through a health production function to improve her health. These commodities could be medical care, which could be seen as repair expenditures, or factors such as diet and exercise, which could be seen as ongoing additions to her health—the counterparts of adding savings to her financial capital on a regular basis. The individual was assumed to make decisions about her level of consumption of these commodities as part of an intertemporal utility-maximizing process that incorporated, through a budget constraint, the need to make tradeoffs between health-related goods and goods that had no health consequences. Pauline Ippolito showed that the same analytical techniques could be used to consider goods that were bad for health in the long run—bad diet and smoking, for example—still within the context of lifetime utility maximization. This raised the possibility that an individual might rationally take actions that were bad for her health in the long run. The logical extension of considering smoking as bad was adding recognition that smoking and other bad health habits were addictive. The notion of addictive commodities was already present in the literature on consumer behavior, but the consensus in that literature was that it was extremely difficult, if not impossible, to distinguish between a rational addict and a completely myopic consumer of addictive goods. Gary Becker and Kevin Murphy proposed an alternative approach to modeling a forward-looking, utility-maximizing consumer’s consumption of addictive commodities, based on the argument that an individual’s degree of addiction could be modeled as addiction capital, and which could be used to tackle the empirical problems that the consumer expenditure literature had experienced. That model has become the most widely used framework for empirical research by economists into the consumption of addictive goods, and, while the concept of rationality in addiction remains controversial, the Becker-Murphy framework also provides a basis for testing various alternative models of the consumption of addictive commodities, most notably those based on versions of time-inconsistent intertemporal decision making.

Article

Multi-Criteria Decision Analysis (MCDA) in Healthcare Decision-Making  

Paul Hansen and Nancy Devlin

Multi-criteria decision analysis (MCDA) is increasingly used to support healthcare decision-making. MCDA involves decision makers evaluating the alternatives under consideration based on the explicit weighting of criteria relevant to the overarching decision—in order to, depending on the application, rank (or prioritize) or choose between the alternatives. A prominent example of MCDA applied to healthcare decision-making that has received a lot of attention in recent years and is the main subject of this article is choosing which health “technologies” (i.e., drugs, devices, procedures, etc.) to fund—a process known as health technology assessment (HTA). Other applications include prioritizing patients for surgery, prioritizing diseases for R&D, and decision-making about licensing treatments. Most applications are based on weighted-sum models. Such models involve explicitly weighting the criteria and rating the alternatives on the criteria, with each alternative’s “performance” on the criteria aggregated using a linear (i.e., additive) equation to produce the alternative’s “total score,” by which the alternatives are ranked. The steps involved in a MCDA process are explained, including an overview of methods for scoring alternatives on the criteria and weighting the criteria. The steps are: structuring the decision problem being addressed, specifying criteria, measuring alternatives’ performance, scoring alternatives on the criteria and weighting the criteria, applying the scores and weights to rank the alternatives, and presenting the MCDA results, including sensitivity analysis, to decision makers to support their decision-making. Arguments recently advanced against using MCDA for HTA and counterarguments are also considered. Finally, five questions associated with how MCDA for HTA is operationalized are discussed: Whose preferences are relevant for MCDA? Should criteria and weights be decision-specific or identical for repeated applications? How should cost or cost-effectiveness be included in MCDA? How can the opportunity cost of decisions be captured in MCDA? How can uncertainty be incorporated into MCDA?

Article

New Technologies and Costs in Healthcare  

Vincenzo Atella and Joanna Kopinska

New sanitation and health technology applied to treatments, procedures, and devices is constantly revolutionizing epidemiological patterns. Since the early 1900s it has been responsible for significant improvements in population health by turning once-deadly diseases into curable or preventable conditions, by expanding the existing cures to more patients and diseases, and by simplifying procedures for both medical and organizational practices. Notwithstanding the benefits of technological progress for the population health, the innovation process is also an important driver of health expenditure growth across all countries. The technological progress generates additional financial burden and expands the volume of services provided, which constitutes a concern from an economic point of view. Moreover, the evolution of technology costs and their impact on healthcare spending is difficult to predict due to the revolutionary nature of many innovations and their adoption. In this respect, the challenge for policymakers is to discourage overadoption of ineffective, unnecessary, and inappropriate technologies. This task has been long carried out through regulation, which according to standard economic theory is the only response to market failures and socially undesirable outcomes of healthcare markets left on their own. The potential welfare loss of a market failure must be confronted with the costs of regulatory activities. While health technology evolution delivers important value for patients and societies, it will continue to pose important challenges for already overextended public finances.

Article

Noncompliance and Missing Data in Health Economic Evaluation  

Karla DiazOrdaz and Richard Grieve

Health economic evaluations face the issues of noncompliance and missing data. Here, noncompliance is defined as non-adherence to a specific treatment, and occurs within randomized controlled trials (RCTs) when participants depart from their random assignment. Missing data arises if, for example, there is loss-to-follow-up, survey non-response, or the information available from routine data sources is incomplete. Appropriate statistical methods for handling noncompliance and missing data have been developed, but they have rarely been applied in health economics studies. Here, we illustrate the issues and outline some of the appropriate methods with which to handle these with application to health economic evaluation that uses data from an RCT. In an RCT the random assignment can be used as an instrument-for-treatment receipt, to obtain consistent estimates of the complier average causal effect, provided the underlying assumptions are met. Instrumental variable methods can accommodate essential features of the health economic context such as the correlation between individuals’ costs and outcomes in cost-effectiveness studies. Methodological guidance for handling missing data encourages approaches such as multiple imputation or inverse probability weighting, which assume the data are Missing At Random, but also sensitivity analyses that recognize the data may be missing according to the true, unobserved values, that is, Missing Not at Random. Future studies should subject the assumptions behind methods for handling noncompliance and missing data to thorough sensitivity analyses. Modern machine-learning methods can help reduce reliance on correct model specification. Further research is required to develop flexible methods for handling more complex forms of noncompliance and missing data.

Article

Pay-for-Performance and Long-Term Care  

Jun Li and Edward C. Norton

Pay-for-performance programs have become a prominent supply-side intervention to improve quality and decrease spending in health care, touching upon long-term care, acute care, and outpatient care. Pay-for-performance directly targets long-term care, with programs in nursing homes and home health. Indirectly, pay-for-performance programs targeting acute care settings affect clinical practice for long-term care providers through incentives for collaboration across settings. As a whole, pay-for-performance programs entail the identification of problems it seeks to solve, measurement of the dimensions it seeks to incentivize, methods to combine and translate performance to incentives, and application of the incentives to reward performance. For the long-term care population, pay-for-performance programs must also heed the unique challenges specific to the sector, such as patients with complex health needs and distinct health trajectories, and be structured to recognize the challenges of incentivizing performance improvement when there are multiple providers and payers involved in the care delivery. Although empirical results indicate modest effectiveness of pay-for-performance in long-term care on improving targeted measures, some research has provided more clarity on the role of pay-for-performance design on the output of the programs, highlighting room for future research. Further, because health care is interconnected, the indirect effects of pay-for-performance programs on long-term care is an underexplored topic. As the scope of pay-for-performance in long-term care expands, both within the United States and internationally, pay-for-performance offers ample opportunities for future research.

Article

Peer Effects in Education  

Andrés Barrios-Fernandez

The identification of peer effects is challenging. There are many factors not related to social influences that could explain correlations among peers. Peers have been shown to affect many important outcomes, including academic performance and educational trajectories. Confirming the existence of peer effects is important from a policy perspective. Both the cost-benefit analysis and the design of policies are likely to be affected by the existence of social spillovers. However, making general policy recommendations from the current evidence is not easy. The size of the peer effects documented in the literature varies substantially across settings and depends on how peers are defined and characterized. Understanding what is behind this heterogeneity is thus key to extract more general policy lessons. Access to better data and the ability to map social networks will likely facilitate investigating which peers and which characteristics matter the most in different contexts. Conducting more research on the mechanisms behind peer effects is also important. Understanding these drivers is key to take advantage of social spillovers in the design of new educational programs, to identify competing policies, and to gain a deeper understanding of the nature and relevance of different forms of social interactions for the youth.

Article

Population Issues in Welfare Economics, Ethics, and Policy Evaluation  

Kevin Kuruc, Mark Budolfson, and Dean Spears

Nearly all large policy decisions influence not only the quality of life for existing individuals but also the number—and even identities—of yet-to-exist individuals. Accounting for these effects in a policy evaluation framework requires taking difficult stances on concepts such as the value of existence. These issues are at the heart of a literature that sits between welfare economics and philosophical population ethics. Despite the inherent challenges of these questions, this literature has produced theoretical insights and subsequent progress on variable-population welfare criteria. A surprisingly bounded set of coherent alternatives exists for practitioners once a set of uncontroversial axioms is adopted from the better-studied welfare criteria for cases where populations are assumed to be fixed. Although consensus has not yet been reached among these remaining alternatives, their recommendations often agree. The space has been sufficiently restricted and well explored that applications of the theoretical insights are possible and underway in earnest. For reasons both theoretical and empirical, the applied literature studying optimal policy and its robustness to welfare criteria has documented a surprising degree of convergence between recommendations under quite different ethical stances on existence value. This convergence has appeared even in cases where population size itself is the choice variable. Although it may not always be the case that policy recommendations are invariant to population welfare criteria, tools have been developed that allow researchers to easily and transparently move between such criteria to study the robustness in their context of interest. The broader point is that the remaining theoretical uncertainties need not prevent population ethics from playing a role in policy evaluation—the tools are available for determining whether and which policies are broadly supported among a range of ethical views.

Article

Poverty and Social Policy in the United States  

James P. Ziliak

The interaction between poverty and social policy is an issue of longstanding interest in academic and policy circles. There are active debates on how to measure poverty, including where to draw the threshold determining whether a family is deemed to be living in poverty and how to measure resources available. These decisions have profound impacts on our understanding of the anti-poverty effectiveness of social welfare programs. In the context of the United States, focusing solely on cash income transfers shows little progress against poverty over the past 50 years, but substantive gains are obtained if the resource concept is expanded to include in-kind transfers and refundable tax credits. Beyond poverty, the research literature has examined the effects of social welfare policy on a host of outcomes such as labor supply, consumption, health, wealth, fertility, and marriage. Most of this work finds the disincentive effects of welfare programs on work, saving, and family structure to be small, but the income and consumption smoothing benefits to be sizable, and some recent work has found positive long-term effects of transfer programs on the health and education of children. More research is needed, however, on how to measure poverty, especially in the face of deteriorating quality of household surveys, on the long-term consequences of transfer programs, and on alternative designs of the welfare state.