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
Aparajita Dasgupta and Anisha Sharma
Article
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
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
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
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
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
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
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
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.
Article
Payment systems based on fixed prices have become the dominant model to finance hospitals across OECD countries. In the early 1980s, Medicare in the United States introduced the diagnosis-related group (DRG) system. The idea was that hospitals should be paid a fixed price for treating a patient within a given diagnosis or treatment. The system then spread to other European countries (e.g., France, Germany, Italy, Norway, Spain, the United Kingdom) and high-income countries (e.g., Canada, Australia). The change in payment system was motivated by concerns over rapid health expenditure growth and replaced financing arrangements based on reimbursing costs (e.g., in the United States) or fixed annual budgets (e.g., in the United Kingdom).
A more recent policy development is the introduction of pay-for-performance (P4P) schemes, which, in most cases, pay directly for higher quality. This is also a form of regulated price payment but the unit of payment is a (process or outcome) measure of quality, as opposed to activity, that is admitting a patient with a given diagnosis or a treatment.
Fixed price payment systems, either of the DRG type or the P4P type, affect hospital incentives to provide quality, contain costs, and treat the right patients (allocative efficiency). Quality and efficiency are ubiquitous policy goals across a range of countries.
Fixed price regulation induces providers to contain costs and, under certain conditions (e.g., excess demand), offer some incentives to sustain quality. But payment systems in the health sector are complex. Since its inception, DRG systems have been continuously refined. From their initial (around) 500 tariffs, many DRG codes have been split in two or more finer ones to reflect heterogeneity in costs within each subgroup. In turn, this may give incentives to provide excessive intensive treatments or to code patients in more remunerative tariffs, a practice known as upcoding. Fixed prices also make it financially unprofitable to treat high cost patients. This is particularly problematic when patients with the highest costs have the largest benefits from treatment. Hospitals also differ systematically in costs and other dimensions, and some of these external differences are beyond their control (e.g., higher cost of living, land, or capital). Price regulation can be put in place to address such differences.
The development of information technology has allowed constructing a plethora of quality indicators, mostly process measures of quality and in some cases health outcomes. These have been used both for public reporting, to help patients choose providers, but also for incentive schemes that directly pay for quality. P4P schemes are attractive but raise new issues, such as they might divert provider attention and unincentivized dimensions of quality might suffer as a result.
Article
A. McGuire
Pharmaceutical expenditure accounts for approximately 20% of healthcare expenditure across the Organisation for Economic Cooperation and Development (OECD) countries. Pharmaceutical products are regulated in all major global markets primarily to ensure product quality but also to regulate the reimbursed prices of insurance companies and central purchasing authorities that dominate this sector. Price regulation is justified as patent protection, which acts as an incentive to invest in R&D given the difficulties in appropriating the returns to such activity and creates monopoly rights to suppliers. Price regulation does itself reduce the ability of producers’ to recapture the substantial R&D investment costs incurred. Traditional price regulation through Ramsey pricing and yardstick competition is not efficient given the distortionary impact of insurance holdings, which are extensive in this sector and the inherent uncertainties that characterize Research and Development (R&D) activity. A range of other pricing regulations aimed at establishing pharmaceutical reimbursement that covers both dynamic efficiency (tied to R&D incentives) and static efficiency (tied to reducing monopoly rents) have been suggested. These range from cost-plus pricing, to internal and external reference pricing, rate-of-return pricing and, most recently value-based (essential health benefit maximization) pricing. Reimbursed prices reflecting value based pricing are, in some countries, associated with clinical treatment guidelines and cost-effectiveness analysis. Some countries are also requiring or allowing post-launch price regulation thorough a range of patient access agreements based on predefined population health targets and/or financial incentives. There is no simple, single solution to the determination of dynamic and static efficiency in this sector given the uncertainty associated with innovation, the large monopoly interests in the area, the distortionary impact of health insurance and the informational asymmetries that exist across providers and purchasers.
Article
Rita Faria
Economic evaluation provides a framework to help inform decisions on which technologies represent the best use of healthcare resources (i.e., are cost-effective) by bringing together the available evidence about the benefits and costs of the alternative options. Critical to the economic evaluation framework is the need to accurately characterize the decision problem—this is the problem-structuring stage. Problem structuring encompasses the characterization of the target population; identification of the decision options to compare in the model (e.g., use of the technology in different ways, standard of care, etc.); and the development of the conceptual model, which maps out how the decision options relate to the costs and benefits in the target population. Problem structuring is central to the application of the economic evaluation framework and to development of the analysis, as it determines the specific questions that can be addressed and affects the relevance and credibility of the results. The methodological guidelines discuss problem structuring to some extent, although the practical implications warrant further consideration. With respect to the target population, questions emerge about how to define it, whether and which sources of heterogeneity to consider, and when and in whom to consider spillovers. Relating to the specification of decision options are questions about how to identify and select them, including restricting the comparison to standard of care, sequences of tests and/or treatments, and “do-nothing” approaches. There are also issues relating to the role and the process of development of the conceptual model. Based on a review of methodological guidelines and reflections on their implications, various recommendations for practice emerge. The process of developing the conceptual model and how to use it to inform choices and assumptions in the economic evaluation are two areas where further research is warranted.
Article
Rosella Levaggi
The concept of soft budget constraint, describes a situation where a decision maker finds it impossible to keep an agent to a fixed budget. In healthcare it may refer to a (nonprofit) hospital that overspends, or to a lower government level that does not balance its accounts. The existence of a soft budget constraint may represent an optimal policy from the regulator point of view only in specific settings. In general, its presence may allow for strategic behavior that changes considerably its nature and its desirability. In this article, soft budget constraint will be analyzed along two lines: from a market perspective and from a fiscal federalism perspective.
The creation of an internal market for healthcare has made hospitals with different objectives and constraints compete together. The literature does not agree on the effects of competition on healthcare or on which type of organizations should compete. Public hospitals are often seen as less efficient providers, but they are also intrinsically motivated and/or altruistic. Competition for quality in a market where costs are sunk and competitors have asymmetric objectives may produce regulatory failures; for this reason, it might be optimal to implement soft budget constraint rules to public hospitals even at the risk of perverse effects. Several authors have attempted to estimate the presence of soft budget constraint, showing that they derive from different strategic behaviors and lead to quite different outcomes.
The reforms that have reshaped public healthcare systems across Europe have often been accompanied by a process of devolution; in some countries it has often been accompanied by widespread soft budget constraint policies. Medicaid expenditure in the United States is becoming a serious concern for the Federal Government and the evidence from other states is not reassuring. Several explanations have been proposed: (a) local governments may use spillovers to induce neighbors to pay for their local public goods; (b) size matters: if the local authority is sufficiently big, the center will bail it out; equalization grants and fiscal competition may be responsible for the rise of soft budget constraint policies. Soft budget policies may also derive from strategic agreements among lower tiers, or as a consequence of fiscal imbalances. In this context the optimal use of soft budget constraint as a policy instrument may not be desirable.
Article
Joanna Coast and Manuela De Allegri
Qualitative methods are being used increasingly by health economists, but most health economists are not trained in these methods and may need to develop expertise in this area. This article discusses important issues of ontology, epistemology, and research design, before addressing the key issues of sampling, data collection, and data analysis in qualitative research. Understanding differences in the purpose of sampling between qualitative and quantitative methods is important for health economists, and the key notion of purposeful sampling is described. The section on data collection covers in-depth and semistructured interviews, focus-group discussions, and observation. Methods for data analysis are then discussed, with a particular focus on the use of inductive methods that are appropriate for economic purposes. Presentation and publication are briefly considered, before three areas that have seen substantial use of qualitative methods are explored: attribute development for discrete choice experiment, priority-setting research, and health financing initiatives.
Article
Matteo Lippi Bruni, Irene Mammi, and Rossella Verzulli
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.
Article
Gregory N. Price
In 1894, W. E. B. Dubois completed coursework for a doctorate in economics at the University of Berlin, and in 1921, Sadie Alexander was the first Black American to earn a doctorate in economics at the University of Pennsylvania. Notwithstanding these rare early accomplishments by Black Americans in economics, there seems to be a more than one century “color line” in the hiring of Black economists in the United States academic labor market. The persistence of Black economist underrepresentation in economics faculties in the United States suggests that a color line constraining the hiring of Black economics faculty endures. In general, and in particular among economics doctorate–granting institutions in the United States, when sorting them by the number of Black Americans on the economics faculty, the median economics department has no Black economics faculty. Findings from the extant literature on the hiring and representation of Black economists suggest that the underrepresentation of Black PhD economists in economics faculties is consistent with, and conforms to, a history of racially discriminatory employment exclusion. This color line could be constraining the production of economics knowledge that can inform public policies that would reduce racial inequality and improve the material living standards of Black Americans in the United States. Future research on the underrepresentation of Black PhD economists in economics faculties in the United States could potentially benefit from accounting for unobservables that may matter for the supply and demand of Black PhD economists. This includes, but is not limited to, what is not observed about individual PhD economist mentoring experiences and parental occupational backgrounds.
Article
Samuel Berlinski and Marcos Vera-Hernández
Socioeconomic gradients in health, cognitive, and socioemotional skills start at a very early age. Well-designed policy interventions in the early years can have a great impact in closing these gaps. Advancing this line of research requires a thorough understanding of how households make human capital investment decisions on behalf of their children, what their information set is, and how the market, the environment, and government policies affect them. A framework for this research should describe how children’s skills evolve and how parents make choices about the inputs that model child development, as well as the rationale for government interventions, including both efficiency and equity considerations.
Article
Jeanet Sinding Bentzen
Economics of religion is the application of economic methods to the study of causes and consequences of religion. Ever since Max Weber set forth his theory of the Protestant ethic, social scientists have compared socioeconomic differences across Protestants and Catholics, Muslims, and Christians, and more recently across different intensities of religiosity. Religiosity refers to an individual’s degree of religious attendance and strength of beliefs. Religiosity rises with a growing demand for religion resulting from adversity and insecurity or a surging supply of religion stemming from increasing numbers of religious organizations, for instance. Religiosity has fallen in some Western countries since the mid-20th century, but has strengthened in several other societies around the world. Religion is a multidimensional concept, and religiosity has multiple impacts on socioeconomic outcomes, depending on the dimension observed. Religion covers public religious activities such as church attendance, which involves exposure to religious doctrines and to fellow believers, potentially strengthening social capital and trust among believers. Religious doctrines teach belief in supernatural beings, but also social views on hard work, refraining from deviant activities, and adherence to traditional norms. These norms and social views are sometimes orthogonal to the general tendency of modernization, and religion may contribute to the rising polarization on social issues regarding abortion, LGBT rights, women, and immigration. These norms and social views are again potentially in conflict with science and innovation, incentivizing some religious authorities to curb scientific progress. Further, religion encompasses private religious activities such as prayer and the particular religious beliefs, which may provide comfort and buffering against stressful events. At the same time, rulers may exploit the existence of belief in higher powers for political purposes. Empirical research supports these predictions. Consequences of higher religiosity include more emphasis on traditional values such as traditional gender norms and attitudes against homosexuality, lower rates of technical education, restrictions on science and democracy, rising polarization and conflict, and lower average incomes. Positive consequences of religiosity include improved health and depression rates, crime reduction, increased happiness, higher prosociality among believers, and consumption and well-being levels that are less sensitive to shocks.
Article
Ijeoma Peace Edoka
Low- and middle-income countries (LMICs) bear a disproportionately high burden of diseases in comparison to high-income countries, partly due to inequalities in the distribution of resources for health. Recent increases in health spending in these countries demonstrate a commitment to tackling the high burden of disease. However, evidence on the extent to which increased spending on health translates to better population health outcomes has been inconclusive. Some studies have reported improvements in population health with an increase in health spending whereas others have either found no effect or very limited effect to justify increased financial allocations to health. Differences across studies may be explained by differences in approaches adopted in estimating returns to health spending in LMICs.
Article
Simon Burgess and Ellen Greaves
School choice and accountability are both mechanisms initially designed to improve standards of education in publicly provided schools, although they have been introduced worldwide with alternative motivations such as to promote equality of access to “good” schools. Economists were active in the initial design of school choice and accountability systems, and continue to advise and provide evidence to school authorities to improve the functioning of the “quasi-market.”
School choice, defined broadly, is any system in which parents’ preferences over schools are an input to their child’s allocation to school. Milton Friedman initially hypothesized that school choice would increase the diversity of education providers and improve schools’ productivity through competition. As in the healthcare sector and other public services, “quasi-markets” can respond to choice and competition by improving standards to attract consumers. Theoretical and empirical work have interrogated this prediction and provided conditions for this prediction to hold. Another reason is to promote equality of access to “good” schools and therefore improve social mobility. Rather than school places being rationed through market forces in the form of higher house prices, for example, school choice can promote equality of access to popular schools. Research has typically considered the role of school choice in increasing segregation between different groups of pupils, however, due to differences in parents’ preferences for school attributes and, in some cases, the complexity of the system.
School accountability is defined as the public provision of school-performance information, on a regular basis, in the same format, and using independent metrics. Accountability has two functions: providing incentives for schools, and information for parents and central authorities. School choice and accountability are linked, in that accountability provides information to parents making school choices, and school choice multiplies the incentive effect of public accountability. Research has studied the effect of school accountability on pupils’ attainment and the implications for teachers as an intermediate mechanism.
