Health, Education, and Welfare Economics

An open issue in the economics literature is whether healthcare expenditure (HCE) is so concentrated in the last years before death that the age profiles in spending will change when longevity increases. The seminal article “aging of Population and HealthCare Expenditure: A Red Herring?” by Zweifel and colleagues argued that that age is a distraction in explaining growth in HCE. The argument was based on the observation that age did not predict HCE after controlling for time to death (TTD). The authors were soon criticized for the use of a Heckman selection model in this context. Most of the recent literature makes use of variants of a two-part model and seems to give some role to age as well in the explanation. Age seems to matter more for long-term care expenditures (LTCE) than for acute hospital care. When disability is accounted for, the effects of age and TTD diminish. Not many articles validate their approach by comparing properties of different estimation models. In order to evaluate popular models used in the literature and to gain an understanding of the divergent results of previous studies, an empirical analysis based on a claims data set from Germany is conducted. This analysis generates a number of useful insights. There is a significant age gradient in HCE, most for LTCE, and costs of dying are substantial. These “costs of dying” have, however, a limited impact on the age gradient in HCE. These findings are interpreted as evidence against the red herring hypothesis as initially stated. The results indicate that the choice of estimation method makes little difference and if they differ, ordinary least squares regression tends to perform better than the alternatives. When validating the methods out of sample and out of period, there is no evidence that including TTD leads to better predictions of aggregate future HCE. It appears that the literature might benefit from focusing on the predictive power of the estimators instead of their actual fit to the data within the sample.

The state-of-the-art literature at the interface between experimental and behavioral economics and health economics is reviewed by identifying and discussing 10 areas of potential debate about behavioral experiments in health. By doing so, the different streams and areas of application of the growing field of behavioral experiments in health are reviewed, by discussing which significant questions remain to be discussed, and by highlighting the rationale and the scope for the further development of behavioral experiments in health in the years to come.

In many countries of the world, consumers choose their health insurance coverage from a large menu of often complex options supplied by private insurance companies. Economic benefits of the wide choice of health insurance options depend on the extent to which the consumers are active, well informed, and sophisticated decision makers capable of choosing plans that are well-suited to their individual circumstances. There are many possible ways how consumers’ actual decision making in the health insurance domain can depart from the standard model of health insurance demand of a rational risk-averse consumer. For example, consumers can have inaccurate subjective beliefs about characteristics of alternative plans in their choice set or about the distribution of health expenditure risk because of cognitive or informational constraints; or they can prefer to rely on heuristics when the plan choice problem features a large number of options with complex cost-sharing design. The second decade of the 21st century has seen a burgeoning number of studies assessing the quality of consumer choices of health insurance, both in the lab and in the field, and financial and welfare consequences of poor choices in this context. These studies demonstrate that consumers often find it difficult to make efficient choices of private health insurance due to reasons such as inertia, misinformation, and the lack of basic insurance literacy. These findings challenge the conventional rationality assumptions of the standard economic model of insurance choice and call for policies that can enhance the quality of consumer choices in the health insurance domain.

The global impact of the COVID-19 pandemic has been profound, leaving a significant imprint on physical health, the economy, and mental well-being. Researchers have undertaken empirical investigations across different countries, with a primary focus on understanding the association between lockdown measures—an essential public health intervention—and mental health. These studies aim to discern the causal effect of lockdowns on mental well-being. Three notable studies have adopted natural experiments to explore the causal effect of lockdowns on mental health in diverse countries. Despite variations in their research methodologies, these studies collectively support the conclusion that lockdowns have had detrimental consequences on mental health. Furthermore, they reveal that the intensity of these negative effects varies among distinct population groups. Certain segments of the population, such as women, have borne a more profound burden of the mental health costs associated with lockdown measures. In light of these findings, it becomes imperative to consider the implications for mental health when implementing public health interventions, especially during crises like the COVID-19 pandemic. While rigorous measures like lockdowns are essential for safeguarding public health, striking a balance with robust mental health support policies becomes crucial to mitigating the adverse impacts on mental well-being.

Stated preference methods are used to collect individual-level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article is data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.

The objective of medical screening is to prevent future disease (secondary prevention) or to improve prognosis by detecting the disease at an earlier stage (early detection). This involves examination of individuals with no symptoms of disease. Introducing a screening program is resource demanding, therefore stakeholders emphasize the need for comprehensive evaluation, where costs and health outcomes are reasonably balanced, prior to population-based implementation. Economic evaluation of population-based screening programs involves quantifying health benefits (e.g., life-years gained) and monetary costs of all relevant screening strategies. The alternative strategies can vary by starting- and stopping-age, frequency of the screening and follow-up regimens after a positive test result. Following evaluation of all strategies, the efficiency frontier displays the efficient strategies and the country-specific cost-effectiveness threshold is used to determine the optimal, i.e., most cost-effective, screening strategy. Similar to other preventive interventions, the costs of screening are immediate, while the health benefits accumulate after several years. Hence, the effect of discounting can be substantial when estimating the net present value (NPV) of each strategy. Reporting both discounting and undiscounted results is recommended. In addition, intermediate outcome measures, such as number of positive tests, cases detected, and events prevented, can be valuable supplemental outcomes to report. Estimating the cost-effectiveness of alternative screening strategies is often based on decision-analytic models, synthesizing evidence from clinical trials, literature, guidelines, and registries. Decision-analytic modeling can include evidence from trials with intermediate or surrogate endpoints and extrapolate to long-term endpoints, such as incidence and mortality, by means of sophisticated calibration methods. Furthermore, decision-analytic models are unique, as a large number of screening alternatives can be evaluated simultaneously, which is not feasible in a randomized controlled trial (RCT). Still, evaluation of screening based on RCT data are valuable as both costs and health benefits are measured for the same individual, enabling more advanced analysis of the interaction of costs and health benefits. Evaluation of screening involves multiple stakeholders and other considerations besides cost-effectiveness, such as distributional concerns, severity of the disease, and capacity influence decision-making. Analysis of harm-benefit trade-offs is a useful tool to supplement cost-effectiveness analyses. Decision-analytic models are often based on 100% participation, which is rarely the case in practice. If those participating are different from those not choosing to participate, with regard to, for instance, risk of the disease or condition, this would result in selection bias, and the result in practice could deviate from the results based on 100% participation. The development of new diagnostics or preventive interventions requires re-evaluation of the cost-effectiveness of screening. For example, if treatment of a disease becomes more efficient, screening becomes less cost-effective. Similarly, the introduction of vaccines (e.g., HPV-vaccination for cervical cancer) may influence the cost-effectiveness of screening. With access to individual level data from registries, there is an opportunity to better represent heterogeneity and long-term consequences of screening on health behavior in the analysis.

End-of-life spending is commonly defined as all health costs in the 12 months before death. Typically, the costs represent about 10% of all health expenses in many countries, and there is a large debate about the effectiveness of the spending and whether it should be increased or decreased. Assuming that health spending is effective in improving health, and using a wide definition of benefits from end-of-life spending, several economists have argued for increased spending in the last years of life. Others remain skeptical about the effectiveness of such spending based on both experimental evidence and the observation that geographic within-country variations in spending are not correlated with variations in mortality.

Recent developments in the analysis of inequality in health and healthcare have turned their interest into an explicit normative understanding of the sources of inequalities that calls upon the concept of equality of opportunity. According to this concept, some sources of inequality are more objectionable than others and could represent priorities for policies aiming to reduce inequality in healthcare use, access, or health status. Equality of opportunity draws a distinction between “legitimate” and “illegitimate” sources of inequality. While legitimate sources of differences can be attributed to the consequences of individual effort (i.e. determinants within the individual’s control), illegitimate sources of differences are related to circumstances (i.e. determinants beyond the individual’s responsibility). The study of inequality of opportunity is rooted in social justice research, and the last decade has seen a rapid growth in empirical work using this literature at the core of its approach in both developed and developing countries. Empirical research on inequality of opportunity in health and healthcare is mainly driven by data availability. Most studies in adult populations are based on data from European countries, especially from the UK, while studies analyzing inequalities of opportunity among children are usually based on data from low- or middle-income countries and focus on children under five years old. Regarding the choice of circumstances, most studies have considered social background to be an illegitimate source of inequality in health and healthcare. Geographical dimensions have also been taken into account, but to a lesser extent, and more frequently in studies focusing on children or those based on data from countries outside Europe. Regarding effort variables or legitimate sources of health inequality, there is wide use of smoking-related variables. Regardless of the population, health outcome, and circumstances considered, scholars have provided evidence of illegitimate inequality in health and healthcare. Studies on inequality of opportunity in healthcare are mainly found in children population; this emphasizes the need to tackle inequality as early as possible.

The assessment of health-related quality of life is crucially important in the evaluation of healthcare technologies and services. In many countries, economic evaluation plays a prominent role in informing decision making often requiring preference-based measures (PBMs) to assess quality of life. These measures comprise two aspects: a descriptive system where patients can indicate the impact of ill health, and a value set based on the preferences of individuals for each of the health states that can be described. These values are required for the calculation of quality adjusted life years (QALYs), the measure for health benefit used in the vast majority of economic evaluations. The National Institute for Health and Care Excellence (NICE) has used cost per QALY as its preferred framework for economic evaluation of healthcare technologies since its inception in 1999. However, there is often an evidence gap between the clinical measures that are available from clinical studies on the effect of a specific health technology and the PBMs needed to construct QALY measures. Instruments such as the EQ-5D have preference-based scoring systems and are favored by organizations such as NICE but are frequently absent from clinical studies of treatment effect. Even where a PBM is included this may still be insufficient for the needs of the economic evaluation. Trials may have insufficient follow-up, be underpowered to detect relevant events, or include the wrong PBM for the decision- making body. Often this gap is bridged by “mapping”—estimating a relationship between observed clinical outcomes and PBMs, using data from a reference dataset containing both types of information. The estimated statistical model can then be used to predict what the PBM would have been in the clinical study given the available information. There are two approaches to mapping linked to the structure of a PBM. The indirect approach (or response mapping) models the responses to the descriptive system using discrete data models. The expected health utility is calculated as a subsequent step using the estimated probability distribution of health states. The second approach (the direct approach) models the health state utility values directly. Statistical models routinely used in the past for mapping are unable to consider the idiosyncrasies of health utility data. Often they do not work well in practice and can give seriously biased estimates of the value of treatments. Although the bias could, in principle, go in any direction, in practice it tends to result in underestimation of cost effectiveness and consequently distorted funding decisions. This has real effects on patients, clinicians, industry, and the general public. These problems have led some analysts to mistakenly conclude that mapping always induces biases and should be avoided. However, the development and use of more appropriate models has refuted this claim. The need to improve the quality of mapping studies led to the formation of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Mapping to Estimate Health State Utility values from Non-Preference-Based Outcome Measures Task Force to develop good practice guidance in mapping.

Happiness has become an important concept in economics as a target for government policy at the national level. This is mirrored in an increasing understanding of the microeconomic effects of increased happiness. While correlational studies have for many years documented a relationship between individual-level happiness and productivity, more recent work provides causal evidence that a positive shock to happiness can boost productivity significantly. These studies include three strands of research. The first provides a number of longitudinal surveys that have generated evidence linking happiness to productivity but run the risk of confounding happiness with other related variables that may be driving the relationship. The second includes laboratory experiments that simulate a workplace under tightly controlled conditions, and this strand has established a clear relationship between positive happiness shocks and rises in productivity. The third involves examining experimental field data, which sacrifices the control of laboratory experiments but offers greater realism. However, there is still work to be done generalizing these findings to more complex work environments, especially those that involve cooperative and team-based tasks where increases in happiness may have other consequences.

The significance of social interaction has become an increasingly important part of economic thought and models through the work on peer effects, social norms, and networks. Within this literature, a novel focus of ranking within groups has emerged. The rank of an individual is usually defined as the ordinal position within a specific group. This could be the work environment or a classroom, and much of this literature focuses on rank effects in education settings. The literature studies rank effects for various age groups. There is evidence that a rank position even during early life phases, such as in elementary education, has lasting effects on education outcomes such as test scores or subject specializations, choices during college, and wages. A first-order challenge in the study of rank effects is to separate them from other highly correlated effects. For example, individuals with a high rank academic rank in a group will likely have high academic ability in absolute terms. Papers in this field directly account for measured ability, and so rely on the variation in rank that exists across groups for any given ability measure, that is, a score of 80 in one group would rank the student top, while near the bottom in another. The comparability of achievement measures across settings is key; one commonly employed solution is to account for level differences across settings. While the literature has now established the importance of rank, there are several—potentially non-competing—ideas for the precise behavioral mechanisms of why rank matters so much. Future work will most likely focus on integrating rank effects into the literature on social interactions to discuss implications for optimal group formation.

Parental choice over public schools has become a major policy tool to combat inequality in access to schools. Traditional neighborhood-based assignment is being replaced by school choice programs, broadening families’ access to schools beyond their residential location. Demand and supply in school choice programs are cleared via centralized admissions algorithms. Heterogeneous parental preferences and admissions policies create trade-offs among efficiency and equity. The data from centralized admissions algorithms can be used effectively for credible research design toward better understanding of school effectiveness, which in turn can be used for school portfolio planning and student assignment based on match quality between students and schools.

Economists have long regarded healthcare as a unique and challenging area of economic activity on account of the specialized knowledge of healthcare professionals (HCPs) and the relatively weak market mechanisms that operate. This places a consideration of how motivation and incentives might influence performance at the center of research. As in other domains economists have tended to focus on financial mechanisms and when considering HCPs have therefore examined how existing payment systems and potential alternatives might impact on behavior. There has long been a concern that simple arrangements such as fee-for-service, capitation, and salary payments might induce poor performance, and that has led to extensive investigation, both theoretical and empirical, on the linkage between payment and performance. An extensive and rapidly expanded field in economics, contract theory and mechanism design, had been applied to study these issues. The theory has highlighted both the potential benefits and the risks of incentive schemes to deal with the information asymmetries that abound in healthcare. There has been some expansion of such schemes in practice but these are often limited in application and the evidence for their effectiveness is mixed. Understanding why there is this relatively large gap between concept and application gives a guide to where future research can most productively be focused.

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.

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.

In developed countries, the role of public authorities as financing bodies and regulators of the long-term care sector is pervasive and calls for well-planned and informed policy actions. Poor quality in nursing homes has been a recurrent concern at least since the 1980s and has triggered a heated policy and scholarly debate. The economic literature on nursing home quality has thoroughly investigated the impact of regulatory interventions and of market characteristics on an array of input-, process-, and outcome-based quality measures. Most existing studies refer to the U.S. context, even though important insights can be drawn also from the smaller set of works that covers European countries. The major contribution of health economics to the empirical analysis of the nursing home industry is represented by the introduction of important methodological advances applying rigorous policy evaluation techniques with the purpose of properly identifying the causal effects of interest. In addition, the increased availability of rich datasets covering either process or outcome measures has allowed to investigate changes in nursing home quality properly accounting for its multidimensional features. The use of up-to-date econometric methods that, in most cases, exploit policy shocks and longitudinal data has given researchers the possibility to achieve a causal identification and an accurate quantification of the impact of a wide range of policy initiatives, including the introduction of nurse staffing thresholds, price regulation, and public reporting of quality indicators. This has helped to counteract part of the contradictory evidence highlighted by the strand of works based on more descriptive evidence. Possible lines for future research can be identified in further exploration of the consequences of policy interventions in terms of equity and accessibility to nursing home care.

Health behaviors are a major source of morbidity and mortality in the developed and much of the developing world. The social nature of many of these behaviors, such as eating or using alcohol, and the normative connotations that accompany others (i.e., sexual behavior, illegal drug use) make them quite susceptible to peer influence. This chapter assesses the role of social interactions in the determination of health behaviors. It highlights the methodological progress of the past two decades in addressing the multiple challenges inherent in the estimation of peer effects, and notes methodological issues that still need to be confronted. A comprehensive review of the economics empirical literature—mostly for developed countries—shows strong and robust peer effects across a wide set of health behaviors, including alcohol use, body weight, food intake, body fitness, teen pregnancy, and sexual behaviors. The evidence is mixed when assessing tobacco use, illicit drug use, and mental health. The article also explores the as yet incipient literature on the mechanisms behind peer influence and on new developments in the study of social networks that are shedding light on the dynamics of social influence. There is suggestive evidence that social norms and social conformism lie behind peer effects in substance use, obesity, and teen pregnancy, while social learning has been pointed out as a channel behind fertility decisions, mental health utilization, and uptake of medication. Future research needs to deepen the understanding of the mechanisms behind peer influence in health behaviors in order to design more targeted welfare-enhancing policies.

The spatial dimension of supply and demand factors is a very important feature of healthcare systems. Differences in health and behavior across individuals are due not only to personal characteristics but also to external forces, such as contextual factors, social interaction processes, and global health shocks. These factors are responsible for various forms of spatial patterns and correlation often observed in the data, which are desirable to include in health econometrics models. This article describes a set of exploratory techniques and econometric methods to visualize, summarize, test, and model spatial patterns of health economics phenomena, showing their scientific and policy power when addressing health economics issues characterized by a strong spatial dimension. Exploring and modeling the spatial dimension of the two-sided healthcare provision may help reduce inequalities in access to healthcare services and support policymakers in the design of financially sustainable healthcare systems.

Due to the similarities with the COVID–19 pandemic, there has been a renewed interest in the 1918–1919 influenza pandemic, which represents the most severe pandemic of the 20th century with an estimated total death toll ranging between 30 and 100 million. This rapidly growing literature in economics and economic history has devoted attention to contextual determinants of excess mortality in the pandemic; to the impact of the pandemic on economic growth, inequality, and a range of other outcomes; and to the impact of nonpharmaceutical interventions. Estimating the effects of the pandemic, or the effects of countermeasures, is challenging. There may not be much exogenous variation to go by, and the historical data sets available are typically small and often of questionable quality. Yet the 1918–1919 pandemic offers a unique opportunity to learn how large pandemics play out in the long run. The studies evaluating effects of the pandemic, or of policies enacted to combat it, typically rely on some version of difference-in-differences, or instrumental variables. The assumptions required for these designs to achieve identification of causal effects have rarely been systematically evaluated in this particular historical context. Using a purpose-built dataset covering the entire Swedish population, such an assessment is provided here. The empirical analysis indicates that the identifying assumptions used in previous work may indeed be satisfied. However, the results cast some doubt on the general external validity of previous findings as the analysis fails to replicate several results in the Swedish context. These disagreements highlight the need for additional studies in other populations and contexts which puts the spotlight on further digitization and linkage of historical datasets.