The terms information and risk aversion play central roles in healthcare economics. While risk aversion is among the main reasons for the existence of health insurance, information asymmetries between insured individual and insurance company potentially lead to moral hazard or adverse selection. This has implications for the optimal design of health insurance contracts, but whether there is indeed moral hazard or adverse selection is ultimately an empirical question. Recently, there was even a debate whether the opposite of adverse selection—advantageous selection—prevails. Private information on risk aversion might weigh out information asymmetries regarding risk type and lead to more insurance coverage of healthy individuals (instead of less insurance coverage in adverse selection). Information and risk preferences are important not only in health insurance but more generally in health economics. For instance, they affect health behavior and, consequently, health outcomes. The degree of risk aversion, the ability to perceive risks, and the availability of information about risks partly explain why some individuals engage in unhealthy behavior while others refrain from smoking, drinking, or the like. Information has several dimensions. Apart from information on one’s personal health status, risk preferences, or health risks, consumer information on provider quality or health insurance supply is central in the economics of healthcare. Even though healthcare systems are necessarily highly regulated throughout the world, all systems at least allow for some market elements. These typically include the possibility of consumer choice, for instance, regarding health insurance coverage or choice of medical provider. An important question is whether consumer choice elements work in the healthcare sector—that is, whether consumers actually make rational or optimal decisions—and whether more information can improve decision quality.
Hendrik Schmitz and Svenja Winkler
Sherry Glied and Richard Frank
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.
Murray Z. Frank, Vidhan Goyal, and Tao Shen
The pecking order theory of corporate capital structure developed by states that issuing securities is subject to an adverse selection problem. Managers endowed with private information have incentives to issue overpriced risky securities. But they also understand that issuing such securities will result in a negative price reaction because rational investors, who are at an information disadvantage, will discount the prices of any risky securities the firm issues. Consequently, firms follow a pecking order: use internal resources when possible; if internal funds are inadequate, obtain external debt; external equity is the last resort. Large firms rely significantly on internal finance to meet their needs. External net debt issues finance the minor deficits that remain. Equity is not a significant source of financing for large firms. By contrast, small firms lack sufficient internal resources and obtain external finance. Although much of it is equity, there are substantial issues of debt by small firms. Firms are sorted into three portfolios based on whether they have a surplus or a deficit. About 15% of firm-year observations are in the surplus group. Firms primarily use surpluses to pay down debt. About 56% of firm-year observations are in the balance group. These firms generate internal cash flows that are just about enough to meet their investment and dividend needs. They issue debt, which is just enough to meet their debt repayments. They are relatively inactive in equity markets. About 29% of firm-year observations are in the deficit group. Deficits arise because of a combination of negative profitability and significant investments in both real and financial assets. Some financing patterns in the data are consistent with a pecking order: firms with moderate deficits favor debt issues; firms with very high deficits rely much more on equity than debt. Others are not: many equity-issuing firms do not seem to have entirely used up the debt capacity; some with a surplus issue equity. The theory suggests a sharp discontinuity in financing methods between surplus firms and deficit firms, and another at debt capacity. The literature provides little support for the predicted threshold effects. The theoretical work has shown that adverse selection does not necessarily lead to pecking order behavior. The pecking order is obtained only under special conditions. With both risky debt and equity being issued, there is often scope for many equilibria, and there is no clear basis for selecting among them. A pecking order may or may not emerge from the theory. Several articles show that the adverse selection problem can be solved by certain financing strategies or properly designed managerial contracts and can even disappear in dynamic models. Although adverse selection can generate a pecking order, it can also be caused by agency considerations, transaction costs, tax consideration, or behavioral decision-making considerations. Under standard tests in the literature, these alternative underlying motivations are commonly observationally equivalent.
In many countries of the world, consumers choose their health insurance coverage from a large menu of often complex options supplied by private insurance companies. Economic benefits of the wide choice of health insurance options depend on the extent to which the consumers are active, well informed, and sophisticated decision makers capable of choosing plans that are well-suited to their individual circumstances. There are many possible ways how consumers’ actual decision making in the health insurance domain can depart from the standard model of health insurance demand of a rational risk-averse consumer. For example, consumers can have inaccurate subjective beliefs about characteristics of alternative plans in their choice set or about the distribution of health expenditure risk because of cognitive or informational constraints; or they can prefer to rely on heuristics when the plan choice problem features a large number of options with complex cost-sharing design. The second decade of the 21st century has seen a burgeoning number of studies assessing the quality of consumer choices of health insurance, both in the lab and in the field, and financial and welfare consequences of poor choices in this context. These studies demonstrate that consumers often find it difficult to make efficient choices of private health insurance due to reasons such as inertia, misinformation, and the lack of basic insurance literacy. These findings challenge the conventional rationality assumptions of the standard economic model of insurance choice and call for policies that can enhance the quality of consumer choices in the health insurance domain.
Mahendrarajah Nimalendran and Giovanni Petrella
The most important friction studied in the microstructure literature is the adverse selection borne by liquidity providers when facing traders who are better informed, and the bid-ask spread quoted by market makers is one of these frictions in securities markets that has been extensively studied. In the early 1980s, the transparency of U.S. stock markets was limited to post-trade end-of-day transactions prices, and there were no easily available market quotes for researchers and market participants to study the effects of bid-ask spread on the liquidity and quality of markets. This led to models that used the auto-covariance of daily transactions prices to estimate the bid-ask spread. In the early 1990s, the U.S. stock markets (NYSE/AMEX/NASDAQ) provided pre-trade quotes and transaction sizes for researchers and market participants. The increased transparency and access to quotes and trades led to the development of theoretical models and empirical methods to decompose the bid-ask spread into its components: adverse selection, inventory, and order processing. These models and methods can be broadly classified into those that use the serial covariance properties of quotes and transaction prices, and others that use a trade direction indicator and a regression approach to decompose the bid-ask spread. Covariance and trade indicator models are equivalent in structural form, but they differ in parameters’ estimation (reduced form). The basic microstructure model is composed of two equations; the first defines the law of motion of the “true” price, while the second defines the process generating transaction price. From these two equations, an appropriate relation for transaction price changes is derived in terms of observed variables. A crucial point that differentiates the two approaches is the assumption made for estimation purposes relative to the behavior of order arrival, which is the probability of order reversal or continuation. Thus, the specification of the most general models allows for including an additional parameter that accounts for order behavior. The article provides a unified framework to compare the different models with respect to the restrictions that are imposed, and how this affects the relative proportions of the different components of the spread.