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The idea that prices and exchange rates adjust so as to equalize the common-currency price of identical bundles of goods—purchasing power parity (PPP)—is a topic of central importance in international finance. If PPP holds continuously, then nominal exchange rate changes do not influence trade flows. If PPP does not hold in the short run, but does in the long run, then monetary factors can affect the real exchange rate only temporarily. Substantial evidence has accumulated—with the advent of new statistical tests, alternative data sets, and longer spans of data—that purchasing power parity does not typically hold in the short run. One reason why PPP doesn’t hold in the short run might be due to sticky prices, in combination with other factors, such as trade barriers. The evidence is mixed for the longer run. Variations in the real exchange rate in the longer run can also be driven by shocks to demand, arising from changes in government spending, the terms of trade, as well as wealth and debt stocks. At time horizon of decades, trend movements in the real exchange rate—that is, systematically trending deviations in PPP—could be due to the presence of nontraded goods, combined with real factors such as differentials in productivity growth. The well-known positive association between the price level and income levels—also known as the “Penn Effect”—is consistent with this channel. Whether PPP holds then depends on the time period, the time horizon, and the currencies examined.

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.

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.

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.

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.

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 recent “replication crisis” in the social sciences has led to increased attention on what statistically significant results entail. There are many reasons for why false positive results may be published in the scientific literature, such as low statistical power and “researcher degrees of freedom” in the analysis (where researchers when testing a hypothesis more or less actively seek to get results with p < .05). The results from three large replication projects in psychology, experimental economics, and the social sciences are discussed, with most of the focus on the last project where the statistical power in the replications was substantially higher than in the other projects. The results suggest that there is a substantial share of published results in top journals that do not replicate. While several replication indicators have been proposed, the main indicator for whether a results replicates or not is whether the replication study using the same statistical test finds a statistically significant effect (p < .05 in a two-sided test). For the project with very high statistical power the various replication indicators agree to a larger extent than for the other replication projects, and this is most likely due to the higher statistical power. While the replications discussed mainly are experiments, there are no reasons to believe that the replicability would be higher in other parts of economics and finance, if anything the opposite due to more researcher degrees of freedom. There is also a discussion of solutions to the often-observed low replicability, including lowering the p value threshold to .005 for statistical significance and increasing the use of preanalysis plans and registered reports for new studies as well as replications, followed by a discussion of measures of peer beliefs. Recent attempts to understand to what extent the academic community is aware of the limited reproducibility and can predict replication outcomes using prediction markets and surveys suggest that peer beliefs may be viewed as an additional reproducibility indicator.

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.

This article covers methodological issues related to estimation, testing, and computation for models involving structural changes. Our aim is to review developments as they relate to econometric applications based on linear models. Substantial advances have been made to cover models at a level of generality that allow a host of interesting practical applications. These include models with general stationary regressors and errors that can exhibit temporal dependence and heteroskedasticity, models with trending variables and possible unit roots and cointegrated models, among others. Advances have been made pertaining to computational aspects of constructing estimates, their limit distributions, tests for structural changes, and methods to determine the number of changes present. A variety of topics are covered including recent developments: testing for common breaks, models with endogenous regressors (emphasizing that simply using least-squares is preferable over instrumental variables methods), quantile regressions, methods based on Lasso, panel data models, testing for changes in forecast accuracy, factors models, and methods of inference based on a continuous records asymptotic framework. Our focus is on the so-called off-line methods whereby one wants to retrospectively test for breaks in a given sample of data and form confidence intervals about the break dates. The aim is to provide the readers with an overview of methods that are of direct use in practice as opposed to issues mostly of theoretical interest.

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.

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.

The rise in obesity and other food-related chronic diseases has prompted public-health officials of local communities, national governments, and international institutions to pay attention to the regulation of food supply and consumer behavior. A wide range of policy interventions has been proposed and tested since the early 21st century in various countries. The most prominent are food taxation, health education, nutritional labeling, behavioral interventions at point-of-decision, advertising, and regulations of food quality and trade. While the standard neoclassical approach to consumer rationality provides limited arguments in favor of public regulations, the recent development of behavioral economics research extends the scope of regulation to many marketing practices of the food industry. In addition, behavioral economics provides arguments in favor of taxation, easy-to-use front-of-pack labels, and the use of nudges for altering consumer choices. A selective but careful review of the empirical literature on taxation, labeling, and nudges suggests that a policy mixing these tools may produce some health benefits. More specifically, soft-drink taxation, front-of-pack labeling policies, regulations of marketing practices, and eating nudges based on affect or behavior manipulations are often effective methods for reducing unhealthy eating.

The economic research faces important challenges. First, the lack of a proper control group and exogenous sources of variations in policy variables make evaluation very difficult. Identification is challenging as well, with data covering short time periods over which markets are observed around slowly moving equilibrium. In addition, truly exogenous supply or demand shocks are rare events. Second, structural models of consumer choices cannot provide accurate assessment of the welfare benefits of public policies because they consider perfectly rational agents and often ignore the dynamic aspects of food decisions, especially consumer concerns over health. Being able to obtain better welfare evaluation of policies is a priority. Third, there is a lack of research on the food industry response to public policies. Some studies implement empirical industrial organization models to infer the industry strategic reactions from market data. A fruitful avenue is to extend this approach to analyze other key dimensions of industrial strategies, especially decisions regarding the nutritional quality of food. Finally, the implementation of nutritional policies yields systemic consequences that may be underestimated. They give rise to conflicts between public health and trade objectives and alter the business models of the food sector. This may greatly limit the external validity of ex-ante empirical approaches. Future works may benefit from household-, firm-, and product-level data collected in rapidly developing economies where food markets are characterized by rapid transitions, the supply is often more volatile, and exogenous shocks occur more frequently.

In the last several decades obesity rates have risen significantly. In 2014, 10.8% and 14.9% of the world’s men and women, respectively, were obese as compared with 3.2% and 6.4% in 1975. The obesity “epidemic” has spread from high-income countries to emerging and developing ones in every region of the world. The rising obesity rates are essentially explained by a rise in total calorie intake associated with long-term global changes in the food supply. Food has become more abundant, available, and cheaper, but food affluence is associated with profound changes in the nutritional quality of supply. While calories have become richer in fats, sugar, and sodium, they are now lower in fiber. The nutrition transition from starvation to abundance and high-fat/sugar/salt food is thus accompanied by an epidemiological transition from infectious diseases and premature death to chronic diseases and longer lives. Food-related chronic diseases have important economic consequences in terms of human capital and medical care costs borne by public and private insurances and health systems.

Technological innovations, trade globalization, and retailing expansion are associated with these substantial changes in the quantity and quality of food supply and diet in developed as well as in emerging and rapidly growing economies. Food variety has significantly increased due to innovations in the food production process. Raw food is broken down to obtain elementary substances that are subsequently assembled for producing final food products. This new approach, as well as improvements in cold chain and packaging, has contributed to a globalization of food chains and spurred an increase of trade in food products, which, jointly with foreign direct investments, alters the domestic food supply. Finally, technological advancements have also favored the emergence of large supermarkets and retailers, which have transformed the industrial organization of consumer markets.

How do these developments affect population diets and diet-related diseases? Identifying the contribution of supply factors to long-term changes in diet and obesity is important because it can help to design innovative, effective, and evidence-based policies, such as regulations on trade, retailing, and quality or incentives for product reformulation. Yet this requires a correct evaluation of the importance and causal effects of supply-side factors on the obesity pandemic. Among others, the economic literature analyzes the effect of changes in food prices, food availability, trade, and marketing on the nutrition and epidemiological transitions. There is a lack of causal robust evidence on their long-term effects. The empirical identification of causal effects is de facto challenging because the dynamics of food supply is partly driven by demand-side factors and dynamics, like a growing female labor force, habit formation, and the social dynamics of preferences.

There are several important limitations to the literature from the early 21st century. Existing studies cover mostly well-developed countries, use static economic and econometric specifications, and employ data that cover short periods of time unmarked by profound shifts in food supply. In contrast, empirical research on the long-term dynamics of consumer behavior is much more limited, and comparative studies across diverse cultural and institutional backgrounds are almost nonexistent. Studies on consumers in emerging countries could exploit the rapid time changes and large spatial heterogeneity, both to identify the causal impacts of shocks on supply factors and to document how local culture and institutions shape diet and nutritional outcomes.

A set of policies is at the center of the agenda on early childhood development: parenting programs, childcare regulation and subsidies, cash and in-kind transfers, and parental leave policies. Incentives are embedded in these policies, and households react to them differently. They also have varying effects on child development, both in developed and developing countries. We have learned much about the impact of these policies in the past 20 years. We know that parenting programs can enhance child development, that centre based care might increase female labor force participation and child development, that parental leave policies beyond three months don’t cause improvement in children outcomes, and that the effects of transfers depend much on their design. In this review, we focus on the incentives embedded in these policies, and how they interact with the context and decision makers to understand the heterogeneity of effects and the mechanisms through which these policies work. We conclude by identifying areas of future research.

A recent body of literature on quantitative general equilibrium models links the creation and diffusion of knowledge and technology to openness to international trade and to the activity of multinational firms. The unifying theme of this literature is methodological: productivities are Fréchet random variables and arise from Poisson innovation and diffusion processes for ideas. The main advantage of this modeling strategy is that it delivers closed-form solutions for key endogenous variables that have a direct counterpart in the data (e.g., prices, trade flows). This tractability makes the connection between theory and data transparent, helps clarify the determinants of the gains from openness, and facilitates the calculation of counterfactual equilibria.

Mental health economics addresses problems that are common to all of health economics, but that occur with greater severity in this context. Several characteristics of mental health conditions—age of onset, chronicity, observability, and external effects—make them particularly economically challenging, and a range of policies have evolved to address these problems. The need for insurance—and for social insurance—to address mental health problems has grown. There is an expanding number of effective treatments available for mental health conditions, and these treatments can be relatively costly. The particular characteristics of mental health conditions exacerbate the usual problems of moral hazard, adverse selection, and agency. There is increased recognition, in both the policy and economics literatures, of the array of services and supports required to enable people with severe mental illnesses to function in society’s mainstream. The need for such non-medical services, generates economic problems of cross-system coordination and opportunism. Moreover, the impairments imposed by mental disorders have become more disruptive to the labor market because the nature of work is changing in a manner that creates special disadvantages to people with these conditions. New directions for mental health economics would address these effects.

Law and economics has proved a particularly fruitful scholarly approach in the field of mergers and acquisitions. A huge law and economics literature has developed, providing critical insights into merger activity in general and the proper role of corporate and securities law in regulating this activity.

Early economic research examined the motivations for merger activity and the antitrust implications of mergers. Later scholarship elucidated the important disciplining effects on management from merger activity and the market for corporate control. If management performs poorly, causing a firm to become undervalued relative to a well-managed firm, the firm becomes vulnerable to a takeover where management will be replaced. This prospect provides a powerful incentive for management to perform well.

More recent work has revealed the limitations of market discipline on management actions in the merger context, and the corresponding role of corporate law in protecting stockholders. Because a merger is generally the final interaction between management and the other stakeholders in a firm, the typical constraints and mechanisms of accountability that otherwise constrain managerial opportunism may be rendered ineffective. This work has played a central role in informing modern jurisprudence. It has shaped the application of enhanced judicial scrutiny of management actions in the merger context, as embodied in the landmark Delaware cases Unocal and Revlon. The law and economics literature has also made important contribution to more recent developments in stockholder appraisal. The law and economics tradition has also provided a useful framework for evaluating the dynamics of merger litigation, including stockholder appraisal, and the extent to which such litigation can be made to serve a useful role in corporate governance.

Life-cycle choices and outcomes over financial (e.g., savings, portfolio, work) and health-related variables (e.g., medical spending, habits, sickness, and mortality) are complex and intertwined. Indeed, labor/leisure choices can both affect and be conditioned by health outcomes, precautionary savings is determined by exposure to sickness and longevity risks, where the latter can both be altered through preventive medical and leisure decisions. Moreover, inevitable aging induces changes in the incentives and in the constraints for investing in one’s own health and saving resources for old age. Understanding these pathways poses numerous challenges for economic models.

The life-cycle data is indicative of continuous declines in health statuses and associated increases in exposure to morbidity, medical expenses, and mortality risks, with accelerating post-retirement dynamics. Theory suggests that risk-averse and forward-looking agents should rely on available instruments to insure against these risks. Indeed, market- and state-provided health insurance (e.g., Medicare) cover curative medical expenses. High end-of-life home and nursing-home expenses can be hedged through privately or publicly provided (e.g., Medicaid) long-term care insurance. The risk of outliving one’s financial resources can be hedged through annuities. The risk of not living long enough can be insured through life insurance.

In practice, however, the recourse to these hedging instruments remains less than predicted by theory. Slow-observed wealth drawdown after retirement is unexplained by bequest motives and suggests precautionary motives against health-related expenses. The excessive reliance on public pension (e.g., Social Security) and the post-retirement drop in consumption not related to work or health are both indicative of insufficient financial preparedness and run counter to consumption smoothing objectives. Moreover, the capacity to self-insure through preventive care and healthy habits is limited when aging is factored in. In conclusion, the observed health and financial life-cycle dynamics remain challenging for economic theory.

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.

The value of a statistical life (VSL) is the local tradeoff rate between fatality risk and money. When the tradeoff values are derived from choices in market contexts the VSL serves as both a measure of the population’s willingness to pay for risk reduction and the marginal cost of enhancing safety. Given its fundamental economic role, policy analysts have adopted the VSL as the economically correct measure of the benefit individuals receive from enhancements to their health and safety. Estimates of the VSL for the United States are around $10 million ($2017), and estimates for other countries are generally lower given the positive income elasticity of the VSL. Because of the prominence of mortality risk reductions as the justification for government policies the VSL is a crucial component of the benefit-cost analyses that are part of the regulatory process in the United States and other countries. The VSL is also foundationally related to the concepts of value of a statistical life year (VSLY) and value of a statistical injury (VSI), which also permeate the labor and health economics literatures. Thus, the same types of valuation approaches can be used to monetize non-fatal injuries and mortality risks that pose very small effects on life expectancy. In addition to formalizing the concept and measurement of the VSL and presenting representative estimates for the United States and other countries our Encyclopedia selection addresses the most important questions concerning the nuances that are of interest to researchers and policymakers.