361-365 of 365 Results


Globalization, Trade, and Health Economics  

Richard Smith and Johanna Hanefeld

Global trade—the movement of goods, services, people, and capital between countries—is at the center of modern globalization. Since the late 20th century trade has also become established as a critical determinant of public health. As the raison d’être of trade is to increase both wealth and the availability of goods and services, changing trade patterns will inevitably impact many of the known determinants of health, including employment, nutrition, environmental factors, social capital, and education. Trade will also impact the health sector itself, most clearly through direct trade in health-related goods and services (such as pharmaceuticals, health workers, foreign direct investment in health services, and mobile patients), but also more broadly in determining tax receipts and thus overall public expenditures. It is also the case that trade—especially rapid and widespread movement of people, animals, and goods—may facilitate the rapid and widespread spread of disease. Trade, and associated policies governing and responding to that trade, has thus become increasingly recognized as a critical driver of health issues. The design of trade policies that reduce the potential health risks associated with freer trade while maximizing the positive impact of trade liberalization on the social determinants of health is still in its infancy. There remains a lack of sound empirical evidence demonstrating how trade liberalization links directly and indirectly to health. Even though the positive link between increased trade, poverty reduction, and economic growth is widely accepted, evidence regarding the impact of trade liberalization on the social determinants of health varies from one national context to another. Hence, adapting trade liberalization to national conditions is important in ensuring desired outcomes. Yet although evidence is necessary, it is not sufficient to ensure that health is more integrated in trade negotiations and decision-making. There is a substantive requirement for those with a health remit to engage in negotiation with those from other sectors and from other geographic locations.


Health Status Measurement  

John Mullahy

Health status measurement issues arise across a wide spectrum of applications in empirical health economics research as well as in public policy, clinical, and regulatory contexts. It is fitting that economists and other researchers working in these domains devote scientific attention to the measurement of those phenomena most central to their investigations. While often accepted and used uncritically, the particular measures of health status used in empirical investigations can have sometimes subtle but nonetheless important implications for research findings and policy action. How health is characterized and measured at the individual level and how such individual-level measures are summarized to characterize the health of groups and populations are entwined considerations. Such measurement issues have become increasingly salient given the wealth of health data available from population surveys, administrative sources, and clinical records in which researchers may be confronted with competing options for how they go about characterizing and measuring health. While recent work in health economics has seen significant advances in the econometric methods used to estimate and interpret quantities like treatment effects, the literature has seen less focus on some of the central measurement issues necessarily involved in such exercises. As such, increased attention ought to be devoted to measuring and understanding health status concepts that are relevant to decision makers’ objectives as opposed to those that are merely statistically convenient.


Mixed Frequency Models  

Eric Ghysels

The majority of econometric models ignore the fact that many economic time series are sampled at different frequencies. A burgeoning literature pertains to econometric methods explicitly designed to handle data sampled at different frequencies. Broadly speaking these methods fall into two categories: (a) parameter driven, typically involving a state space representation, and (b) data driven, usually based on a mixed-data sampling (MIDAS)-type regression setting or related methods. The realm of applications of the class of mixed frequency models includes nowcasting—which is defined as the prediction of the present—as well as forecasting—typically the very near future—taking advantage of mixed frequency data structures. For multiple horizon forecasting, the topic of MIDAS regressions also relates to research regarding direct versus iterated forecasting.


Qualitative Methods in Health Economics  

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.


Outlier Detection  

Bent Nielsen

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.