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.
George W. Evans and Bruce McGough
Adaptive learning is a boundedly rational alternative to rational expectations that is increasingly used in macroeconomics, monetary economics, and financial economics. The agent-level approach can be used to provide microfoundations for adaptive learning in macroeconomics. Two central issues of bounded rationality are simultaneously addressed at the agent level: replacing fully rational expectations of key variables with econometric forecasts and boundedly optimal decisions-making based on those forecasts. The real business cycle (RBC) model provides a useful laboratory for exhibiting alternative implementations of the agent-level approach. Specific implementations include shadow-price learning (and its anticipated-utility counterpart, iterated shadow-price learning), Euler-equation learning, and long-horizon learning. For each implementation the path of the economy is obtained by aggregating the boundedly rational agent-level decisions. A linearized RBC can be used to illustrate the effects of fiscal policy. For example, simulations can be used to illustrate the impact of a permanent increase in government spending and highlight the similarities and differences among the various implements of agent-level learning. These results also can be used to expose the differences among agent-level learning, reduced-form learning, and rational expectations. The different implementations of agent-level adaptive learning have differing advantages. A major advantage of shadow-price learning is its ease of implementation within the nonlinear RBC model. Compared to reduced-form learning, which is widely use because of its ease of application, agent-level learning both provides microfoundations, which ensure robustness to the Lucas critique, and provides the natural framework for applications of adaptive learning in heterogeneous-agent models.