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The literature on optimum currency areas differs from that on other topics in economic theory in a number of notable respects. Most obviously, the theory is framed in verbal rather than mathematical terms. Mundell’s seminal article coining the term and setting out the theory’s basic propositions relied entirely on words rather than equations. The same was true of subsequent contributions focusing on the sectoral composition of activity and the role of fiscal flows. A handful of more recent articles specified and analyzed formal mathematical models of optimum currency areas. But it is safe to say that none of these has “taken off” in the sense of becoming the workhorse framework on which subsequent scholarship builds. The theoretical literature remains heavily qualitative and narrative compared to other areas of economic theory. While Mundell, McKinnon, Kenen, and the other founding fathers of optimum-currency-area theory provided powerful intuition, attempts to further formalize that intuition evidently contributed less to advances in economic understanding than has been the case for other theoretical literatures.
Second, recent contributions to the literature on optimum currency areas are motivated to an unusual extent by a particular case, namely Europe’s monetary union. This was true already in the 1990s, when the EU’s unprecedented decision to proceed with the creation of the euro highlighted the question of whether Europe was an optimum currency area and, if not, how it might become one. That tendency was reinforced when Europe then descended into crisis starting in 2009. With only slight exaggeration it can be said that the literature on optimum currency areas became almost entirely a literature on Europe and on that continent’s failure to satisfy the relevant criteria.
Third, the literature on optimum currency areas remains the product of its age. When the founders wrote, in the 1960s, banks were more strictly regulated, and financial markets were less internationalized than subsequently. Consequently, the connections between monetary integration and financial integration—whether monetary union requires banking union, as the point is now put—were neglected in the earlier literature. The role of cross-border financial flows as a destabilizing mechanism within a currency area did not receive the attention it deserved. Because much of that earlier literature was framed in a North American context—the question was whether the United States or Canada was an optimum currency area—and because it was asked by a trio of scholars, two of whom hailed from Canada and one of whom hailed from the United States, the challenges of reconciling monetary integration with political nationalism and the question of whether monetary requires political union were similarly underplayed. Given the euro area’s descent into crisis, a number of analysts have asked why economists didn’t sound louder warnings in advance. The answer is that their outlooks were shaped by a literature that developed in an earlier era when the risks and context were different.
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article.
Detection of outliers is an important explorative step in empirical analysis. Once detected, the investigator will have to decide how to model the outliers depending on the context. Indeed, the outliers may represent noisy observations that are best left out of the analysis or they may be very informative observations that would have a particularly important role in the analysis. For regression analysis in time series a number of outlier algorithms are available, including impulse indicator saturation and methods from robust statistics. The algorithms are complex and their statistical properties are not fully understood. Extensive simulation studies have been made, but the formal theory is lacking. Some progress has been made toward an asymptotic theory of the algorithms. A number of asymptotic results are already available building on empirical process theory.
Jesús Gonzalo and Jean-Yves Pitarakis
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article.
Predictive regressions refer to models whose aim is to assess the predictability of a typically noisy time series, such as stock returns or currency returns with past values of a highly persistent predictor such as valuation ratios, interest rates, or volatilities, among other variables. Obtaining reliable inferences through conventional methods can be challenging in such environments mainly due to the joint interactions of predictor persistence, potential endogeneity, and other econometric complications. Numerous methods have been developed in the literature ranging from adjustments to test statistics used in significance testing to alternative instrumental variable based estimation methods specifically designed to neutralize inferences to the stochastic properties of the predictor(s).
Early developments in this area were mainly confined to linear and single predictor settings, but recent developments have raised the issue of adaptability of existing estimation and inference methods to more general environments so as to extend the use of predictive regressions to a wider range of potential applications.
An important extension involves allowing predictability to enter nonlinearly so as to capture time variation in the role of particular predictors. Economically interesting nonlinearities include, for instance, the use of threshold effects that allow predictability to vanish or strengthen during particular episodes, creating pockets of predictability. Such effects may kick in in the conditional means but also in the variances or both and may help uncover important phenomena such as the countercyclical nature of stock return predictability recently documented in the literature.
Due to the frequent need to consider multiple as opposed to single predictors it also becomes important to evaluate the validity and feasibility of inferences about linear and nonlinear predictability when multiple predictors of potentially different degrees of persistence are allowed to coexist in such settings.
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 Groups (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.
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, 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.
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.
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.
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.
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.
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?