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Article

Nikolaus Robalino and Arthur Robson

Modern economic theory rests on the basic assumption that agents’ choices are guided by preferences. The question of where such preferences might have come from has traditionally been ignored or viewed agnostically. The biological approach to economic behavior addresses the issue of the origins of economic preferences explicitly. This approach assumes that economic preferences are shaped by the forces of natural selection. For example, an important theoretical insight delivered thus far by this approach is that individuals ought to be more risk averse to aggregate than to idiosyncratic risk. Additionally the approach has delivered an evolutionary basis for hedonic and adaptive utility and an evolutionary rationale for “theory of mind.” Related empirical work has studied the evolution of time preferences, loss aversion, and explored the deep evolutionary determinants of long-run economic development.

Article

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?

Article

Iñigo Hernandez-Arenaz and Nagore Iriberri

Gender differences, both in entering negotiations and when negotiating, have been proved to exist: Men are usually more likely to enter into negotiation than women and when negotiating they obtain better deals than women. These gender differences help to explain the gender gap in wages, as starting salaries and wage increases or promotions throughout an individual’s career are often the result of bilateral negotiations. This article presents an overview of the literature on gender differences in negotiation. The article is organized in four main parts. The first section reviews the findings with respect to gender differences in the likelihood of engaging in a negotiation, that is, in deciding to start a negotiation. The second section discusses research on gender differences during negotiations, that is, while bargaining. The third section looks at the relevant psychological literature and discusses meta-analyses, looking for factors that trigger or moderate gender differences in negotiation, such as structural ambiguity and cultural traits. The fourth section presents a brief overview of research on gender differences in non- cognitive traits, such as risk and social preferences, confidence, and taste for competition, and their impact in explaining gender differences in bargaining. Finally, the fifth section discusses some policy implications. An understanding of when gender differences are likely to arise on entering into negotiations and when negotiating will enable policies to be created that can mitigate current gender differences in negotiations. This is an active, promising research line.

Article

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.

Article

Denzil G. Fiebig and Hong Il Yoo

Stated preference methods are used to collect individual-level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article is data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.

Article

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.

Article

The assessment of health-related quality of life is crucially important in the evaluation of healthcare technologies and services. In many countries, economic evaluation plays a prominent role in informing decision making often requiring preference-based measures (PBMs) to assess quality of life. These measures comprise two aspects: a descriptive system where patients can indicate the impact of ill health, and a value set based on the preferences of individuals for each of the health states that can be described. These values are required for the calculation of quality adjusted life years (QALYs), the measure for health benefit used in the vast majority of economic evaluations. The National Institute for Health and Care Excellence (NICE) has used cost per QALY as its preferred framework for economic evaluation of healthcare technologies since its inception in 1999. However, there is often an evidence gap between the clinical measures that are available from clinical studies on the effect of a specific health technology and the PBMs needed to construct QALY measures. Instruments such as the EQ-5D have preference-based scoring systems and are favored by organizations such as NICE but are frequently absent from clinical studies of treatment effect. Even where a PBM is included this may still be insufficient for the needs of the economic evaluation. Trials may have insufficient follow-up, be underpowered to detect relevant events, or include the wrong PBM for the decision- making body. Often this gap is bridged by “mapping”—estimating a relationship between observed clinical outcomes and PBMs, using data from a reference dataset containing both types of information. The estimated statistical model can then be used to predict what the PBM would have been in the clinical study given the available information. There are two approaches to mapping linked to the structure of a PBM. The indirect approach (or response mapping) models the responses to the descriptive system using discrete data models. The expected health utility is calculated as a subsequent step using the estimated probability distribution of health states. The second approach (the direct approach) models the health state utility values directly. Statistical models routinely used in the past for mapping are unable to consider the idiosyncrasies of health utility data. Often they do not work well in practice and can give seriously biased estimates of the value of treatments. Although the bias could, in principle, go in any direction, in practice it tends to result in underestimation of cost effectiveness and consequently distorted funding decisions. This has real effects on patients, clinicians, industry, and the general public. These problems have led some analysts to mistakenly conclude that mapping always induces biases and should be avoided. However, the development and use of more appropriate models has refuted this claim. The need to improve the quality of mapping studies led to the formation of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Mapping to Estimate Health State Utility values from Non-Preference-Based Outcome Measures Task Force to develop good practice guidance in mapping.

Article

In recent decades, there has been a dramatic proliferation of preferential trade agreements (PTAs) between countries that, while legal, contradict the non-discrimination principle of the world trade system. This raises various issues, both theoretical and empirical, regarding the evolution of trade policy within the world trade system and the welfare implications for PTA members and non-members. The survey starts with the Kemp-Wan-Ohyama and Panagariya-Krishna analyses in the literature that theoretically show PTAs can always be constructed so that they (weakly) increase the welfare of members and non-members. Considerable attention is then devoted to recent developments on the interaction between PTAs and multilateral trade liberalization, focusing on two key incentives: an “exclusion incentive” of PTA members and a “free riding incentive” of PTA non-members. While the baseline presumption one should have in mind is that these incentives lead PTAs to inhibit the ultimate degree of global trade liberalization, this presumption can be overturned when dynamic considerations are taken into account or when countries can negotiate the degree of multilateral liberalization rather than facing a binary choice over global free trade. Promising areas for pushing this theoretical literature forward include the growing use of quantitative trade models, incorporating rules of origin and global value chains, modeling the issues surrounding “mega-regional” agreements, and modelling the possibility of exit from PTAs. Empirical evidence in the literature is mixed regarding whether PTAs lead to trade diversion or trade creation, whether PTAs have significant adverse effects on non-member terms-of-trade, whether PTAs lead members to lower external tariffs on non-members, and the role of PTAs in facilitating deep integration among members.