Dynamic Consistency In Welfare Analysis

Papers motivated by dynamic inconsistency

As explained in the introduction to this chapter, dynamic inconsistency poses a difficulty for welfare analysis, since the individual reveals different preferences at different points in time and it is unclear which of these set of preferences, if any, should be regarded as representing his welfare. An early approach to welfare evaluation in the presence of dynamic inconsistency is called the multi-selves Pareto criterion (e.g., Laibson et al. <cite>laibson1998self</cite>). This criterion is rationalized by regarding the individual as a collection of different selves. It asserts that a change in consumption is a welfare improvement if and only if it is acceptable by all selves; that is, increases
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</footnote> it is readily verified that applying it to the infinitely many periods version of the β, δ model, would result in the long-run criterion. In point of fact, the same conclusion holds even when postponing the consumption plans only by one period. As pointed by BR (p. 57-58), the arm 's length perspective might, in some cases, provide a better guidance for welfare because it does not trigger the psychological processes responsible for apparent lapses of self-control but in other cases it provides poor guidance for welfare due to various reasons such as imperfect imagination and indecisiveness.
<footnote>We believe that when it is a lack of self-control that makes us suspicious about whether particular choices are a good guidance for welfare, this should be modeled explicitly (as we do in Chapter 4).
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did not provide a formal model of intertemporal welfare evaluation, they did suggest a detailed methodology to be applied to behavioral choice data for eliciting normative (welfare) preferences. In what follows we explain their methodology in detail. Step 1, involves the building of a positive behavioral model incorporating all of the economic and psychological motives that shape behavior, including the mechanisms that generate mistakes. In step 2, a set of normative axioms is constructed to produce a mapping rule from the behavioral model to a normative model; step 3 uses structural estimation techniques to calibrate the model according to the data available (on ensuring that the positive model describes the data well). Finally, step 4 applies the mapping constructed in step 2 to produce the individual normative

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