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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.
Thomas J. Kniesner and W. Kip Viscusi
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.
High-dimensional dynamic factor models have their origin in macroeconomics, more specifically in empirical research on business cycles. The central idea, going back to the work of Burns and Mitchell in the 1940s, is that the fluctuations of all the macro and sectoral variables in the economy are driven by a “reference cycle,” that is, a one-dimensional latent cause of variation. After a fairly long process of generalization and formalization, the literature settled at the beginning of the 2000s on a model in which (a) both n, the number of variables in the data set, and T, the number of observations for each variable, may be large; (b) all the variables in the data set depend dynamically on a fixed, independent of n, number of common shocks, plus variable-specific, usually called idiosyncratic, components. The structure of the model can be exemplified as follows:
where the observable variables are driven by the white noise , which is common to all the variables, the common shock, and by the idiosyncratic component . The common shock is orthogonal to the idiosyncratic components , the idiosyncratic components are mutually orthogonal (or weakly correlated). Last, the variations of the common shock affect the variable dynamically, that is, through the lag polynomial . Asymptotic results for high-dimensional factor models, consistency of estimators of the common shocks in particular, are obtained for both and tending to infinity.
The time-domain approach to these factor models is based on the transformation of dynamic equations into static representations. For example, equation () becomes
Instead of the dynamic equation () there is now a static equation, while instead of the white noise there are now two factors, also called static factors, which are dynamically linked:
This transformation into a static representation, whose general form is
is extremely convenient for estimation and forecasting of high-dimensional dynamic factor models. In particular, the factors and the loadings can be consistently estimated from the principal components of the observable variables .
Assumption allowing consistent estimation of the factors and loadings are discussed in detail. Moreover, it is argued that in general the vector of the factors is singular; that is, it is driven by a number of shocks smaller than its dimension. This fact has very important consequences. In particular, singularity implies that the fundamentalness problem, which is hard to solve in structural vector autoregressive (VAR) analysis of macroeconomic aggregates, disappears when the latter are studied as part of a high-dimensional dynamic factor model.
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?
Mostafa Beshkar and Eric Bond
International trade agreements have played a significant role in the reduction of trade barriers that has taken place since the end of World War II. One objective of the theoretical literature on trade agreements is to address the question of why bilateral and multilateral trade agreements, rather than simple unilateral actions by individual countries, have been required to reduce trade barriers. The predominant explanation has been the terms of trade theory, which argues that unilateral tariff policies lead to a prisoner’s dilemma due to the negative effect of a country’s tariffs on its trading partners. Reciprocal tariff reductions through a trade agreement are required to obtain tariff reductions that improve on the noncooperative equilibrium. An alternative explanation, the commitment theory of trade agreements, focuses on the use of external enforcement under a trade agreement to discipline domestic politics.
A second objective of the theoretical literature has been to understand the design of trade agreements. Insights from contract theory are used to study various flexibility mechanisms that are embodied in trade agreements. These mechanisms include contingent protection measures such as safeguards and antidumping, and unilateral flexibility through tariff overhang. The literature also addresses the enforcement of agreements in the absence of an external enforcement mechanism. The theories of the dispute settlement process of the WTO portray it as an institution with an informational role that facilitates the coordination among parties with incomplete information about the states of the world and the nature of the actions taken by each signatory. Finally, the literature examines whether the ability to form preferential trade agreements serves as a stumbling block or a building block to multilateral liberalization.
Urban sprawl in popular sources is vaguely defined and largely misunderstood, having acquired a pejorative meaning. Economists should ask whether particular patterns of urban land use are an outcome of an efficient allocation of resources. Theoretical economic modeling has been used to show that more not less, sprawl often improves economic efficiency. More sprawl can cause a reduction in traffic congestion. Job suburbanization can generally increase sprawl but improves economic efficiency. Limiting sprawl in some cities by direct control of the land use can increase sprawl in other cities, and aggregate sprawl in all cities combined can increase. That urban population growth causes more urban sprawl is verified by empirically implemented general equilibrium models, but—contrary to common belief—the increase in travel times that accompanies such sprawl are very modest. Urban growth boundaries to limit urban sprawl cause large deadweight losses by raising land prices and should be seen to be socially intolerable but often are not. It is good policy to use corrective taxation for negative externalities such as traffic congestion and to implement property tax reforms to reduce or eliminate distortive taxation. Under various circumstances such fiscal measures improve welfare by increasing urban sprawl. The flight of the rich from American central cities, large lot zoning in the suburbs, and the financing of schools by property tax revenues are seen as causes of sprawl. There is also evidence that more heterogeneity among consumers and more unequal income distributions cause more urban sprawl. The connections between agglomeration economies and urban sprawl are less clear. The emerging technology of autonomous vehicles can have major implications for the future of urban spatial structure and is likely to add to sprawl.
Henrik Andersson, Arne Risa Hole, and Mikael Svensson
Many public policies and individual actions have consequences for population health. To understand whether a (costly) policy undertaken to improve population health is a wise use of resources, analysts can use economic evaluation methods to assess the costs and benefits. To do this, it is necessary to evaluate the costs and benefits using the same metric, and for convenience, a monetary measure is commonly used. It is well established that money measures of a reduction in health risks can be theoretically derived using the willingness-to-pay concept. However, because a market price for health risks is not available, analysts have to rely on analytical techniques to estimate the willingness to pay using revealed- or stated-preference methods. Revealed-preference methods infer willingness to pay based on individuals’ actual behavior in markets related to health risks, and they include such approaches as hedonic pricing techniques. Stated-preference methods use a hypothetical market scenario in which respondents make trade-offs between wealth and health risks. Using, for example, a random utility framework, it is possible to directly estimate individuals’ willingness to pay by analyzing the trade-offs they make in the hypothetical scenario. Stated-preference methods are commonly applied using contingent valuation or discrete choice experiment techniques. Despite criticism and the shortcomings of both the revealed- and stated-preference methods, substantial progress has been made since the 1990s in using both approaches to estimate the willingness to pay for health-risk reductions.
Marisa Miraldo, Katharina Hauck, Antoine Vernet, and Ana Wheelock
Major medical innovations have greatly increased the efficacy of treatments, improved patient outcomes, and often reduced the cost of medical care. However, innovations do not diffuse uniformly across and within health systems. Due to the high complexity of medical treatment decisions, variations in clinical practice are inherent to healthcare delivery, regardless of technological advances, new ways of working, funding, and burden of disease. In this article we conduct a narrative literature review to identify and discuss peer-reviewed articles presenting a theoretical framework or empirical evidence of the factors associated with the adoption of innovation and clinical practice.
We find that variation in innovation adoption and medical practice is associated with multiple factors. First, patients’ characteristics, including medical needs and genetic factors, can crucially affect clinical outcomes and the efficacy of treatments. Moreover, differences in patients’ preferences can be an important source of variation. Medical treatments may need to take such patient characteristics into account if they are to deliver optimal outcomes, and consequently, resulting practice variations should be considered warranted and in the best interests of patients. However, socioeconomic or demographic characteristics, such as ethnicity, income, or gender are often not considered legitimate grounds for differential treatment. Second, physician characteristics—such as socioeconomic profile, training, and work-related characteristics—are equally an influential component of practice variation. In particular, so-called “practice style” and physicians’ attitudes toward risk and innovation adoption are considered a major source of practice variation, but have proven difficult to investigate empirically. Lastly, features of healthcare systems—notably, public coverage of healthcare expenditure, cost-based reimbursement of providers, and service-delivery organization, are generally associated with higher utilization rates and adoption of innovation.
Research shows some successful strategies aimed at reducing variation in medical decision-making, such as the use of decision aids, data feedback, benchmarking, clinical practice guidelines, blinded report cards, and pay for performance. But despite these advances, there is uneven diffusion of new technologies and procedures, with potentially severe adverse efficiency and equity implications.