[New study]Does contact tracing work? Quasi-experimental evidence from an Excel error in England with Thiemo Fetzer
Contact tracing has been a central pillar of the public health response to the
COVID-19 pandemic. Yet, contact tracing measures face substantive challenges in practice and well-identified evidence about their effectiveness remains scarce. This paper exploits quasi-random variation in COVID-19 contact tracing. Between September 25 and October 2, 2020, a total of 15,841
COVID-19 cases in England (around 15 to 20% of all cases) were not immediately referred to the contact tracing system due to a data processing error.
Case information was truncated from an Excel spreadsheet after the row limit
had been reached, which was discovered on October 3. There is substantial
variation in the degree to which different parts of England areas were exposed
– by chance – to delayed referrals of COVID-19 cases to to the contact tracing
system. We show that more affected areas subsequently experienced a drastic
rise in new COVID-19 infections and deaths alongside an increase in the positivity rate and the number of test performed, as well as a decline in the performance of the contact tracing system. Conservative estimates suggest that
the failure of timely contact tracing due to the data glitch is associated with
more than 125,000 additional infections and over 1,500 additional COVID-19-
related deaths. Our findings provide strong quasi-experimental evidence for
the effectiveness of contact tracing.
Because many economic decisions are difficult, people may exhibit cognitive uncertainty: subjective uncertainty about what the optimal action is.
In the presence of such cognitive uncertainty, people implicitly compress objective probabilities
towards a cognitive default of 50:50.
By experimentally measuring and exogenously manipulating cognitive uncertainty,
this idea brings together and partially explains a set of striking similarities in well-known behavioral anomalies in choice under risk,
choice under ambiguity, belief updating, and survey forecasts of economic variables.
Structural estimations show that the pronounced inverse S-shaped response patterns that pervade these literatures result from how cognitive uncertainty varies with objective probabilities.
This paper studies how people make inference about a state of the world when the information structure includes additional, payoff-irrelevant states. For example, learning about effort from observed performance may require accounting for the otherwise irrelevant role of luck. This creates an attribution problem that is common to information structures with multiple causes. We report controlled experimental evidence for pervasive overinference about states that affect utility given an action, providing an explanation for a collection of well-known but previously unconnected misattribution patterns. In studying why systematic misattribution arises, we consistently find that errors are not due to excessive task complexity or deliberate effort avoidance. Instead, people form incomplete mental models of the information structure and fail to notice the need to account for alternative causes. These mental models are not stable but context-dependent: misattribution responds to a variety of attentional manipulations, but not to changes in the costs of inattention.
We examine the role of heterogeneity in gain-loss attitudes for identifying the leading behavioral model of expectations-based reference dependence (Kőszegi and Rabin, 2006, 2007) (KR). Failure to account for recently-documented heterogeneity in gain-loss attitudes is a central confounding factor challenging prior tests of KR, all conducted under the assumption of universal loss aversion. We implement a two-stage experimental design to first measure gain-loss attitudes, and, second, examine heterogeneous treatment effects over these attitudes in an exchange paradigm common to prior tests of KR. In both an initial experiment and an exact replication, we document the heterogeneous treatment effects over gain-loss attitudes predicted by the model. These findings provide foundational support for the KR model’s expectations-based mechanism and explain inconsistencies in prior empirical exercises conducted under the assumption of homogeneous loss aversion.
Bayesian signatures of confidence and central tendency in perceptual judgment with Yang Xiang, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the stimulus distribution. Based on a formal Bayesian framework, we show that measures of subjective confidence can be used to explain the central tendency effects and response variability through a Bayesian lens. Specifically, our model clarifies that lower subjective confidence as a measure of posterior uncertainty about a judgment should predict (i) a lower sensitivity of magnitude estimates to objective stimuli; (ii) a higher sensitivity to the mean of the stimulus distribution; (iii) a stronger central tendency effect at higher stimulus magnitudes; and (iv) higher response variability. To test these predictions, we collect a tailored large-scale experimental data set and additionally re-analyze perceptual judgment data from several previous experiments. Across data sets, subjective confidence is strongly predictive of the central tendency effect and response variability, both correlationally and when we exogenously manipulate the magnitude of sensory noise. Our results lend support to Bayesian explanations of both confidence and the central tendency effect.
Does prosocial behavior promote happiness? We test this longstanding hypothesis in a behavioral experiment that extends the
scope of previous research. In our Saving a Life paradigm, every
participant either saved one human life in expectation by triggering a targeted donation of 350 euros or received an amount of 100
euros. Using a choice paradigm between two binary lotteries with
different chances of saving a life, we observed subjects’ intentions at the same time as creating random variation in prosocial
outcomes. We repeatedly measured happiness at various delays.
Our data weakly replicate the positive effect identified in previous research but only for the very short run. One month later, the
sign of the effect reversed, and prosocial behavior led to significantly lower happiness than obtaining the money. Notably, even
those subjects who chose prosocially were ultimately happier if
they ended up getting the money for themselves. Our findings
revealed a more nuanced causal relationship than previously suggested, providing an explanation for the apparent absence of
universal prosocial behavior.