The Role of Second-Best Theory in Public Economics
This paper surveys the evolution of the use of the theory of second best in public economics. It argues that much of modern normative public economics can be interpreted as simply applied second-best analysis. The original theory of second best as expounded by Lipsey and Lancaster involved analyzing policy in a single-consumer economy with a fixed distortion, and was especially interested in whether marginal cost pricing, or piece-meal prescriptions, could still be maintained. That analysis was subsequently extended to multi-household economies, to multi-distortion cases and to dynamic settings, and became the basis for the optimal tax revolution in public economics. However, more significantly, in the wake of optimal tax analysis and duality theory, the second-best distortion has effectively been made endogenous; and the general government policy problem has been posed as a principal-agent one. The most common method is by assuming non-observability of some important household characteristic or behavioral outcome. As a consequence of these developments, most public policy problems can be viewed as special applications of second-best analysis. For example, the general problem of the efficiency-equity trade-off (the `optimal income tax' problem) and the limit to redistribution can be viewed as second-best problems. A couple of the interesting features of viewing policy problems as second-best problems are as follows. For one, simple policy prescriptions no longer become possible. For another, seemingly odd types of policies, such as quantity restrictions, in-kind transfers and public provision of social insurance become `efficient' policy instruments in certain circumstances. The literature also stresses that second-best policies are typically time-inconsistent. In the face of this, standard second-best optima cannot be attained. Optimal time-consistent policies can also include unusual policy instruments that would otherwise be ruled out in a second-best setting.
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