“Shared prosperity” is a common phrase in current development policy discourse. Its most widely used operational definition—the growth rate in the average income of the poorest 40% of a country’s population—is a truncated measure of change in social welfare. A related concept, the shared prosperity premium—the difference between the growth rate of the mean for the bottom 40% and the growth rate in the overall mean—is similarly analogous to a measure of change in inequality. This article reviews the relationship between these concepts and the more established ideas of social welfare, poverty, inequality, and mobility.
Household survey data can be used to shed light on recent progress in terms of this indicator globally. During 2008–2013, mean incomes for the poorest 40% rose in 60 of the 83 countries for which we have data. In 49 of them, accounting for 65% of the sampled population, it rose faster than overall average incomes, thus narrowing the income gap.
In the policy space, there are examples both of “pre-distribution” policies (which promote human capital investment among the poor) and “re-distribution” policies (such as targeted safety nets), which when well-designed have a sound empirical track record of both raising productivity and improving well-being among the poor.
Article
Francisco H. G. Ferreira, Emanuela Galasso, and Mario Negre
Article
Ana Balsa and Carlos Díaz
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.
Article
Elisa Tosetti, Rita Santos, Francesco Moscone, and Giuseppe Arbia
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.
Article
Richard C. van Kleef, Thomas G. McGuire, Frederik T. Schut, and Wynand P. M. M. van de Ven
Many countries rely on social health insurance supplied by competing insurers to enhance fairness and efficiency in healthcare financing. Premiums in these settings are typically community rated per health plan. Though community rating can help achieve fairness objectives, it also leads to a variety of problems due to risk selection, that is, actions by consumers and insurers to exploit “unpriced risk” heterogeneity. From the viewpoint of a consumer, unpriced risk refers to the gap between her expected spending under a health plan and the net premium for that plan. Heterogeneity in unpriced risk can lead to selection by consumers in and out of insurance and between high- and low-value plans. These forms of risk selection can result in upward premium spirals, inefficient take-up of basic coverage, and inefficient sorting of consumers between high- and low-value plans.
From the viewpoint of an insurer, unpriced risk refers to the gap between his expected costs under a certain contract and the revenues he receives for that contract. Heterogeneity in unpriced risk incentivizes insurers to alter their plan offerings in order to attract profitable people, resulting in inefficient plan design and possibly in the unavailability of high-quality care. Moreover, insurers have incentives to target profitable people via marketing tools and customer service, which—from a societal perspective—can be considered a waste of resources.
Common tools to counteract selection problems are risk equalization, risk sharing, and risk rating of premiums. All three strategies reduce unpriced risk heterogeneity faced by insurers and thus diminish selection actions by insurers such as the altering of plan offerings. Risk rating of premiums also reduces unpriced risk heterogeneity faced by consumers and thus mitigates selection in and out of insurance and between high- and low-value plans. All three strategies, however, come with trade-offs. A smart blend takes advantage of the strengths, while reducing the weaknesses of each strategy. The optimal payment system configuration will depend on how a regulator weighs fairness and efficiency and on how the healthcare system is organized.
Article
Brigitte Dormont
Most developed nations provide generous coverage of care services, using either a tax financed healthcare system or social health insurance. Such systems pursue efficiency and equity in care provision. Efficiency means that expenditures are minimized for a given level of care services. Equity means that individuals with equal needs have equal access to the benefit package. In order to limit expenditures, social health insurance systems explicitly limit their benefit package. Moreover, most such systems have introduced cost sharing so that beneficiaries bear some cost when using care services. These limits on coverage create room for private insurance that complements or supplements social health insurance. Everywhere, social health insurance coexists along with voluntarily purchased supplementary private insurance. While the latter generally covers a small portion of health expenditures, it can interfere with the functioning of social health insurance. Supplementary health insurance can be detrimental to efficiency through several mechanisms. It limits competition in managed competition settings. It favors excessive care consumption through coverage of cost sharing and of services that are complementary to those included in social insurance benefits. It can also hinder achievement of the equity goals inherent to social insurance. Supplementary insurance creates inequality in access to services included in the social benefits package. Individuals with high incomes are more likely to buy supplementary insurance, and the additional care consumption resulting from better coverage creates additional costs that are borne by social health insurance. In addition, there are other anti-redistributive mechanisms from high to low risks. Social health insurance should be designed, not as an isolated institution, but with an awareness of the existence—and the possible expansion—of supplementary health insurance.
Article
Francine J. Lipman
Since 2010, Congress has significantly cut the annual budget of the Internal Revenue Service (IRS) while requiring the IRS to manage more responsibilities, including last-minute comprehensive tax reform, health care, broad-based antipoverty relief, and a variety of economic stimulus provisions. As a result, the IRS has sustained across-the-board decreases in staffing, with the most significant decreases in tax enforcement personnel. The IRS has fewer auditors than at any time since World War II, despite an explosion of concentrated income and wealth. Predictably, the tax gap, the difference between what taxpayers owe and what taxpayers pay, has skyrocketed to almost $1 trillion a year. Economists have estimated that funding the IRS will pay for itself severalfold, raising more than a trillion dollars of uncollected tax revenues over a decade.
Despite evidence that funding will remedy budget shortfalls severalfold, Congress continues to defund the IRS. While the bulk of the tax gap is due to unreported income by high-income individuals, the audit rate of these households has dropped precipitously. By comparison, the lowest income wage earners are being audited five times more often than all other taxpayers. Given centuries of racist policies in the United States, households of color are disproportionately impoverished and white households are disproportionately wealthy. Accordingly, lower income working families of color, especially in the South, are audited at rates higher than their white northern counterparts. Moreover, because these households and the IRS have limited resources, many of these audits result in taxpayers losing antipoverty benefits that they have properly claimed. This discriminatory treatment is counter to Congressional intent to support these families and exacerbates existing racial income and wealth gaps. With President Biden’s 2021 executive order on advancing racial equity and support for underserved communities through the federal government, the U.S. Treasury, IRS, and Congress have been charged to “recognize and work to redress inequities in their policies and programs that serve as barriers to equal opportunity.” Properly funding the IRS is a necessary step to advancing racial equity.
Article
Albert A. Okunade and Ahmad Reshad Osmani
Healthcare cost encompasses expenditures on the totality of scarce resources (implicit and explicit) given up (or allocated) to produce healthcare goods (e.g., drugs and medical devices) and services (e.g., hospital care and physician office services are major components). Healthcare cost accounting components (sources and uses of funds) tend to differ but can be similar enough across most of the world countries. The healthcare cost concept usually differs for consumers, politicians and health policy decision-makers, health insurers, employers, and the government. All else given, inefficient healthcare production implies higher economic cost and lower productivity of the resources deployed in the process. Healthcare productivity varies across health systems of the world countries, the production technologies used, regulatory instruments, and institutional settings. Healthcare production often involves some specific (e.g., drugs and medical devices, information and communication technologies) or general technology for diagnosing, treating, or curing diseases in order to improve or restore human health conditions.
In the last half century, the different healthcare systems of the world countries have undergone fundamental transformations in the structural designs, institutional regulations, and socio-economic and demographic dimensions. The nations have allocated a rising share of total economic resources or incomes (i.e., Gross National Product, or GDP) to the healthcare sector and are consequently enjoying substantial increases in population health status and life expectancies. There are complex and interacting linkages among escalating healthcare costs, longer life expectancies, technological progress (or “the march of science”), and sectoral productivities in the health services sectors of the advanced economies. Healthcare policy debates often concentrate on cost-containment strategies and search for improved efficient resource allocation and equitable distribution of the sector’s outputs. Consequently, this contribution is a broad review of the body of literature on technological progress, productivity, and cost: three important dimensions of the evolving modern healthcare systems. It provides a logical integration of three strands of work linking healthcare cost to technology and research evidence on sectoral productivity measurements. Finally, some important aspects of the existing study limitations are noted to motivate new research directions for future investigations to explore in the growing health sector economies.
Article
Martin Karlsson, Daniel Kühnle, and Nikolaos Prodromidis
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.
Article
Sheelah Connolly
In the coming years, it is predicted that there will be a significant increase in the number of people living with dementia and consequently, the demand for health and social care services. Given the budget constraints facing health systems, it is anticipated that economic analysis will play an increasingly important role in informing decisions regarding the provision of services for people with dementia. However, compared with other conditions and diseases, research in dementia has been relatively limited. While in the past this may have been related to an assumption that dementia was a natural part of aging, there are features of dementia that make applying research methods particularly challenging. A number of economic methods have been applied to dementia, including cost-of-illness analysis and economic evaluation; however, methodological issues in this area persist. These include reaching a consensus on how best to measure and value informal care, how to capture the many impacts and costs of the condition as the disease progresses, and how to measure health outcomes. Addressing these existing methodological issues will help realize the potential of economic analysis in answering difficult questions around care for people with dementia.
Article
Judith M. Delaney and Paul J. Devereux
There are two well-established gender gaps in education. First, females tend to have higher educational attainment and achievement than males, and this is particularly the case for children from less advantaged backgrounds. Second, there are large differences in the fields of specialization chosen by males and females in college and even prior to college, and females disproportionately enter less highly paid fields.
Gender differences in noncognitive traits, behavior, and interests have been shown to relate to differences in educational outcomes; however, this evidence cannot generally be given a causal interpretation. In contrast, the literature has been creative in estimating causal impacts of a wide range of factors using experimental and quasiexperimental variation. While the approaches are compelling, the findings vary widely across studies and are often contradictory. This may partly reflect methodological differences across studies, but it also may result from substantial true heterogeneity across educational systems and time periods.
Lower educational achievement of males has been linked to gender differences in attitudes, behaviors, and educational aspirations as well as the tendency of males to mature at older ages. Differential field choices by gender are related to differences in comparative advantage by gender and gender differences in preferences for types of study and work and for nonpecuniary aspects of jobs, such as their flexibility and gender mix. There are reasons to believe that policy should address the two gender gaps, and many possible policy approaches exist. However, their effectiveness is unclear, and there is scope for further work to determine which policies are likely to be effective and in which circumstances.
Article
Osea Giuntella and Timothy J. Halliday
Migration and health are intimately connected. It is known that migrants tend to be healthier than non-migrants. However, the mechanisms for this association are elusive. On the one hand, the costs of migration are lower for healthier people, thereby making it easier for the healthy to migrate. Empirical evidence from a variety of contexts shows that the pre-migration health of migrants is better than it is for non-migrants, indicating that there is positive health-based selection in migration. On the other hand, locations can be viewed as a bundle of traits including but not limited to environmental conditions, healthcare quality, and violence. Each of these can impact health. Evidence shows that moving from locations with high mortality to low mortality can reduce mortality risks. Consistent with this, migration can increase mortality risk if it leads to greater exposure to risk factors for disease. The health benefits enjoyed by migrants can also be found in their children. However, these advantages erode with successive generations.
Article
Marjon van der Pol and Alastair Irvine
The interest in eliciting time preferences for health has increased rapidly since the early 1990s. It has two main sources: a concern over the appropriate methods for taking timing into account in economics evaluations, and a desire to obtain a better understanding of individual health and healthcare behaviors. The literature on empirical time preferences for health has developed innovative elicitation methods in response to specific challenges that are due to the special nature of health. The health domain has also shown a willingness to explore a wider range of underlying models compared to the monetary domain. Consideration of time preferences for health raises a number of questions. Are time preferences for health similar to those for money? What are the additional challenges when measuring time preferences for health? How do individuals in time preference for health experiments make decisions? Is it possible or necessary to incentivize time preference for health experiments?
Article
Henrik Andersson, Arne Risa Hole, and Mikael Svensson
Many public policies and individual actions have consequences for population health. To understand whether a (costly) policy undertaken to improve population health is a wise use of resources, analysts can use economic evaluation methods to assess the costs and benefits. To do this, it is necessary to evaluate the costs and benefits using the same metric, and for convenience, a monetary measure is commonly used. It is well established that money measures of a reduction in health risks can be theoretically derived using the willingness-to-pay concept. However, because a market price for health risks is not available, analysts have to rely on analytical techniques to estimate the willingness to pay using revealed- or stated-preference methods. Revealed-preference methods infer willingness to pay based on individuals’ actual behavior in markets related to health risks, and they include such approaches as hedonic pricing techniques. Stated-preference methods use a hypothetical market scenario in which respondents make trade-offs between wealth and health risks. Using, for example, a random utility framework, it is possible to directly estimate individuals’ willingness to pay by analyzing the trade-offs they make in the hypothetical scenario. Stated-preference methods are commonly applied using contingent valuation or discrete choice experiment techniques. Despite criticism and the shortcomings of both the revealed- and stated-preference methods, substantial progress has been made since the 1990s in using both approaches to estimate the willingness to pay for health-risk reductions.
Article
Jessalynn James and Susanna Loeb
Since the turn of the 21st century, an abundant body of research has demonstrated that teachers meaningfully contribute to their students’ learning but that teachers vary widely in their effectiveness. Measures of teachers’ “value added” to student achievement have become common, and sometimes controversial, tools for researchers and policymakers hoping to identify and differentiate teachers’ individual contributions to student learning. Value-added measures aim to identify how much more a given teacher’s students learn than what would be expected based on how much other, similar students learn with other teachers. The question of how to measure value added without substantial measurement error and without incorrectly capturing other factors outside of teachers’ control is complex and sometime illusory, and the advantages and drawbacks to any particular method of estimating teachers’ value added depend on the specific context and purpose for their use. Traditionally, researchers have calculated value-added scores only for the subset of teachers with students in tested grades and subjects—a relatively small proportion of the teaching force, in a narrow set of the many domains on which teachers may influence their students. More recently, researchers have created value-added estimates for a range of other student outcomes, including measures of students’ engagement and social-emotional learning such as attendance and behavioral incidences, which may be available for more teachers. Overall, teacher value-added measures can be useful tools for understanding and improving teaching and learning, but they have substantial limitations for many uses and contexts.
Article
Marisa Miraldo, Katharina Hauck, Antoine Vernet, and Ana Wheelock
Major medical innovations have greatly increased the efficacy of treatments, improved patient outcomes, and often reduced the cost of medical care. However, innovations do not diffuse uniformly across and within health systems. Due to the high complexity of medical treatment decisions, variations in clinical practice are inherent to healthcare delivery, regardless of technological advances, new ways of working, funding, and burden of disease. In this article we conduct a narrative literature review to identify and discuss peer-reviewed articles presenting a theoretical framework or empirical evidence of the factors associated with the adoption of innovation and clinical practice.
We find that variation in innovation adoption and medical practice is associated with multiple factors. First, patients’ characteristics, including medical needs and genetic factors, can crucially affect clinical outcomes and the efficacy of treatments. Moreover, differences in patients’ preferences can be an important source of variation. Medical treatments may need to take such patient characteristics into account if they are to deliver optimal outcomes, and consequently, resulting practice variations should be considered warranted and in the best interests of patients. However, socioeconomic or demographic characteristics, such as ethnicity, income, or gender are often not considered legitimate grounds for differential treatment. Second, physician characteristics—such as socioeconomic profile, training, and work-related characteristics—are equally an influential component of practice variation. In particular, so-called “practice style” and physicians’ attitudes toward risk and innovation adoption are considered a major source of practice variation, but have proven difficult to investigate empirically. Lastly, features of healthcare systems—notably, public coverage of healthcare expenditure, cost-based reimbursement of providers, and service-delivery organization, are generally associated with higher utilization rates and adoption of innovation.
Research shows some successful strategies aimed at reducing variation in medical decision-making, such as the use of decision aids, data feedback, benchmarking, clinical practice guidelines, blinded report cards, and pay for performance. But despite these advances, there is uneven diffusion of new technologies and procedures, with potentially severe adverse efficiency and equity implications.
