91色情片

Xueting Wang | Quasi-hyperbolic present bias

A meta-analysis
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91色情片 Roundtable meeting in office
Time
13/07/2021 - 12:00 - 13:00
Address
Zoom meeting (online only)

Description

  • July 13, 2021
  • Speaker: Xueting Wang
  • Topic: Quasi-hyperbolic Present Bias: A Meta-analysis

Abstract

Quasi-hyperbolic discounting is one of the most well-known and widely-used models to capture self-control problems in economics. The underlying assumption of the model is that agents have a 鈥減resent bias鈥 toward current consumption such that all future rewards are downweighed relative to rewards in the present (in addition to standard exponential discounting for the length of delay). We report a meta-analytic dataset of estimates of the present bias parameter 脽聽based on searches of all major research databases (62 papers with 81 estimates in total). We find that the literature shows that people are on average present biased towards money (脽 = 0.82, 95% confidence interval of [0.74, 0.90], but that substantial heterogeneity across studies exists.

The source of this heterogeneity comes from the subject pool, elicitation methodology, geographical location, payment method, and mode of data collection (laboratory versus. field). The reward type also has an influence on the estimated present bias: individuals show stronger present bias towards real effort and health outcomes compared to monetary rewards (脽 = 0.66,聽95% confidence interval of [0.51, 0.85]). There is evidence of selective reporting and publication bias in the direction of overestimating the strength of present-bias (i.e. making 脽聽estimates smaller), but present bias still exists after correcting for these issues (the corrected 脽 =聽0.84 with 95% confidence interval of [0.77, 0.92]for money, and 0.72 with 95% confidence interval of [0.49, 0.94] for non-monetary rewards).

About the speaker

Xueting Wang is聽a PhD student at The University of Sydney funded by a scholarship from the聽.聽She is also a Postgraduate Teaching Fellow at The University of Sydney.聽Her research goal is to discover and describe the mechanisms that govern human decision-making.