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Nudging and Phishing: A Theory of Behavioral Welfare Economics

Authors: David Jimenez-Gomez;

Nudging and Phishing: A Theory of Behavioral Welfare Economics

Abstract

Nudges, which are interventions that do not restrict choice, have become widespread in policy applications. I develop a general and tractable framework to analyze the welfare implications of nudges. In this framework, individuals suffer from internalities (their utility when choosing is different from their welfare-determining utility) and choice and welfare depend on the environment, which can be altered by the nudge. I show that, in order to design the optimal nudge, no knowledge of environment-independent preferences is required. This means that the social planner does not need to fully recover individual preferences, something which is especially difficult in the presence of internalities. In heterogeneous populations, the optimal nudge trades off correcting the internalities of biased individuals with psychological costs imposed by the nudge on all individuals. When taxes are also available, nudging is generally optimal as long as the government is not fully efficient in collecting revenue from taxation. I also analyze phishing, when firms change the environment to take advantage of consumers’ internalities. Competition does not necessarily reduce phishing and, when firms have incentives to phish, competition can be welfare-decreasing. I analyze nudging and phishing in general equilibrium, and characterize the optimal nudge. In contrast to recent empirical work, which finds that nudging can backfire in general equilibrium because firms raise prices in response to a nudge, I show that under perfect competition nudging is generally welfare-enhancing.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Top 10%
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