计量经济分析 = Econometric analysis

How to make a decision: The analytic hierarchy process

People make three general types of judgments to express importance, preference, or likelihood and use them to choose the best among alternatives in the presence of environmental, social, political, and other influences. They base these judgments on knowledge in memory or from analyzing benefits, costs, and risks. From past knowledge, we sometimes can develop standards of excellence and poorness and use them to rate the alternatives one at a time. This is useful in such repetitive situations as student admissions and salary raises that must conform with established norms. Without norms one compares alternatives instead of rating them. Comparisons must fall in an admissible range of consistency. The analytic hierarchy process (AHP) includes both the rating and comparison methods. Rationality requires developing a reliable hierarchic structure or feedback network that includes criteria of various types of influence, stakeholders, and decision alternatives to determine the best choice.

How to Make a Decision: The Analytic Hierarchy Process

Dowe really knowwhatwewant?Ormustwe sometimes construct our preferences on the spot, using whatever cues are available – even when these cues lead us astray? One of the main themes that has emerged from behavioral decision research during the past three decades is the view that people’s preferences are often constructed in the process of elicitation. This idea is derived from studies demonstrating that normatively equivalent methods of elicitation (e.g., choice and pricing) give rise to systematically different responses. These preference reversals violate the principle of procedure invariance that is fundamental to all theories of rational choice. If different elicitation procedures produce different orderings of options, how can preferences be defined and in what sense do they exist? This book shows not only the historical roots of preference construction but also the blossoming of the conceptwithin psychology, law,marketing, philosophy, environmental policy, and economics. Decision making is now understood to be a highly contingent form of information processing, sensitive to task complexity, time pressure, response mode, framing, reference points, and other contextual factors.

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The Construction of Preference

Some empirical evidence on dynamic inconsistency

This paper studies the relationship between health care expenditure (HCE) and age, using longitudinal rather than cross-sectional data. The econometric analysis of HCE in the last eight quarters of life of individuals who died during the period 1983-1992 indicates that HCE depends on remaining lifetime but not on calendar age, at least beyond 65+. The positive relationship between age and HCE observed in cross-sectional data may be caused by the simple fact that at age 80, for example, there are many more individuals living in their last 2 years than at age 65. The limited impact of age on HCE suggests that population ageing may contribute much less to future growth of the health care sector than claimed by most observers.

Ageing of population and health care expenditure: a red herring?

Consumer incentives are reflected in a wide range of choices, many of which occur in both insurance- and tax-financed health care systems. However, health insurance and sick leave pay cause consumer incentives to be reflected in moral hazard effects of several types. Theoretically, ex ante moral hazard (a reduction of preventive effort in response to insurance coverage) is not unambiguously predicted, and there is very limited empirical evidence about it. The case for static ex post moral hazard (an increase in the demand for medical care of a given technology) is stronger. The empirical evidence reported comes from three sources, natural experiments, observational comparisons of individuals, and the Health Insurance Experiment (HIE). The distinguishing feature of the HIE is that participants were assigned to insurance plans, which forestalls the possibility of good risks self-selecting plans with substantial cost sharing, resulting in an overestimate of the effects of plan design on health care expenditure. While the values of estimated price elasticities vary widely among the three sources and less markedly according to the type of care (outpatient, hospital, dental, mental), the responsiveness of the demand for medical care to net price is beyond doubt. The pure price elasticity for medical care in excess of a deductible (i.e. where the marginal price is constant) was estimated by HIE at −0.2 overall. Finally, there may be a dynamic moral hazard effect (choice biased in favor of new, usually more expensive medical technology). Here, the empirical evidence is very scanty again. Another promising field for future research is the interplay between consumer incentives and rationing by the physician in managed care.

Moral Hazard and Consumer Incentives in Health Care

This paper revisits the debate on the 'red herring', viz. the claim that population ageing will not have a significant impact on health care expenditure (HCE). It decomposes HCE into seven components, includes both survivors and deceased individuals, and estimates a two-part model of the demand for health care services, using a large Swiss data set for 1999. It finds no or weak age effects on HCE for the components of HCE when proximity to death is controlled for, and points to differences between users and non-users of long-term care (LTC). For deceased non-users of LTC services, a falling age curve for all components of HCE except for inpatient care is observed, while survivors show a weak age effect in ambulatory and inpatient care once proximity to death is controlled for. As to surviving users of LTC services, their probability of incurring LTC expenses markedly increases in old age, while most of the components of their conditional HCE show a decreasing age profile. Thus, a 'school of red herrings' can be claimed to exist-with the possible exception of LTC, where ageing might matter regardless of proximity to death.

Population ageing and health care expenditure: a school of ‘red herrings’?

The observation that average health care expenditure rises with age generally leads experts and laymen alike to conclude that population ageing is the main driver of health care costs. In recently published studies we challenged this view (Zweifel et al., 1999; Felder et al., 2000). Analysing health care expenditure of deceased persons, we showed that age is insignificant if proximity to death is controlled for. Thus, we argued that population ageing per se will not have a significant impact on future health care expenditure. Several authors (Salas and Raftery, 2001; Dow and Norton, 2002; Seshamani and Gray, 2004a) disputed the robustness of these findings, pointing to potential weaknesses in the econometric methodology. This paper revisits the debate and provides new empirical evidence, taking into account the methodological concerns that have been raised. We also include surviving individuals to test for the possibility that the relative importance of proximity to death and age differs between the deceased and survivors. The results vindicate our earlier findings of no significant age effect on health care expenditure of the deceased. However, with respect to the survivors, we find that age may matter. Still, a naive estimation that does not control for proximity to death will grossly overestimate the effect of population ageing on aggregate health care expenditure. Following Stearns and Norton (2004), we conclude that “it is time for time to death” in projections of future health care costs.

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Population ageing and health care expenditure: new evidence on the "red herring"

In a recent issue of this Review, David Grether and Charles Plott (1979, hereinafter referred to as G-P) presented a series of experiments that show that individual decisions are made in a manner inconsistent with standard preference theory. This inconsistency can be illustrated by the following example: Individuals under suitable laboratory conditions first have a choice between a lottery with a high probability of winning a small sum of money, the P bet (probability bet); and a lottery with a high stake but low probability of winning, the M bet (money bet). After indicating a preference for one of the lotteries in the pair, the same subjects are then asked to place a monetary value on each of the individual lotteries. Grether and Plott found that a large number of participants stated a preference for the P bet in the first round; but placed a higher value on the other lottery, the M bet, in the second round. This behavior, a preference reversal, is inconsistent with the traditional statements of preference theory.

Peter Zweifel

Papers

Ageing of population and health care expenditure: a red herring?

Moral Hazard and Consumer Incentives in Health Care

Population ageing and health care expenditure: a school of ‘red herrings’?

Population ageing and health care expenditure: new evidence on the "red herring"

Economic Theory of Choice and the Preference Reversal Phenomenon: A Reexamination