Competitive Credit and Present Bias: A Stochastic Discounting Approach

TL;DR

This paper introduces a stochastic discounting model for competitive credit provision with time-consistent preferences, capturing behaviors like overestimating patience and predicting backloaded consumption, differing from traditional quasi-hyperbolic discounting models.

Contribution

It develops a novel stochastic discounting framework that accounts for present bias and equilibrium contract features, extending beyond existing quasi-hyperbolic models with new predictions.

Findings

Agents overestimate patience, leading to earlier consumption choices.

Equilibrium contracts penalize less patient discount realizations.

Model predicts excessive backloaded consumption even with correct beliefs.

Abstract

A prominent theme in behavioural contract theory is the study of present-biased agents represented through quasi-hyperbolic discounting. In a model of competitive credit provision, we study an alternative to this framework in which the agent has a private stochastic discount factor and may overestimate the likelihood of more patient values. Agent preferences, however, are timeconsistent. While a limiting case of our model corresponds to a "fully naive" agent in work on quasi-hyperbolic discounting, another case is where the agent has correct beliefs about future discounting. In equilibrium, the agent selects options with earlier consumption in case of less patient discount factor realisations, but is penalised by receiving worse terms. Our model thus accounts for an important feature of equilibrium contracts identified in Heidhues and K\H{o}szegi (2010). Unlike Heidhues and K\H{o}szegi, our framework often predicts excessively backloaded consumption, including when the agent holds correct beliefs about future discounting.