# Can Information Shape Macroeconomic Disaster Risk Perception and Stimulate Investment? An Experiment with Experts and Laypersons

Brice Corgnet, Camille Cornand, Pauline Gandré

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# Can Information Shape Macroeconomic Disaster Risk Perception and Stimulate Investment? An Experiment with Experts and Laypersons

Brice Corgnet, Camille Cornand, Pauline Gandré

## Abstract:

Macroeconomic disasters are low-probability events with severe economic and financial consequences. Using a survey experiment with both finance experts and laypersons, we conduct the first incentivized elicitation of beliefs about macroeconomic disaster risk and subsequent investment in risky financial assets. Our findings indicate that, consistent with the systematic overweighting of recent disasters, both laypersons and finance experts overestimate disaster risk, leading to underinvestment in our portfolio task. However, we find that communicating historical information about the frequency of macroeconomic disasters helps to correct misperceptions of this frequency and stimulate investment. Although participants adjust their beliefs in the expected direction, they fail to react to the precision of information while, in the case of laypersons, reacting to the salience of information. Our findings offer practical guidelines for effective communication by financial institutions and show that they depend on the financial expertise of the target population.

## Keywords:

Macroeconomic disasters; Expectations; Communication; Randomized information experiment

## JEL codes:

C9, C11, D83, D91, E71, G11
# Can Information Shape Macroeconomic Disaster Risk Perception and Stimulate Investment? An Experiment with Experts and Laypersons*

Brice Corgnet† Camille Cornand‡ Pauline Gandré§

May 2025

**Abstract:** Macroeconomic disasters are low-probability events with severe economic and financial consequences. Using a survey experiment with both finance experts and laypersons, we conduct the first incentivized elicitation of beliefs about macroeconomic disaster risk and subsequent investment in risky financial assets. Our findings indicate that, consistent with the systematic overweighting of recent disasters, both laypersons and finance experts overestimate disaster risk, leading to underinvestment in our portfolio task. However, we find that communicating historical information about the frequency of macroeconomic disasters helps to correct misperceptions of this frequency and stimulate investment. Although participants adjust their beliefs in the expected direction, they fail to react to the precision of information while, in the case of laypersons, reacting to the salience of information. Our findings offer practical guidelines for effective communication by financial institutions and show that they depend on the financial expertise of the target population.

**JEL classification:** C9, C11, D83, D91, E71, G11.

**Keywords:** Macroeconomic disasters; Expectations; Communication; Randomized information experiment.

<footnote>*This research has benefited from the financial support of the French National Research Agency (ANR) under the French-German project "ASUR" (ANR-18-FRAL-008-01 ASUR). It was performed within the Federation CNRS S2CH. We would like to thank Fabien Cerrotti and Quentin Thévenet for helping us with the design of the computer software, and Mathilde Berregard and Jean-Christophe Vergnaud at the Federation CNRS S2CH. We would also like to thank the associations of finance professionals (the Association Française de Gestion Financière (AFG), the Société Française des Analystes Financiers (SFAF), the Association Française des Investisseurs Institutionnels (AF2i) and the Chartered Financial Analysts (CFA) France) which helped us to reach finance professionals, along with Olivier Armantier, Christophe Boucher, Marie Brière, Muhammed Bulutay, Gaëtan Le Quang, Isabelle Salle, Thomas Valli, Florent Wabont, Mirko Wiederholt, and seminar participants at University Paris Nanterre and the University of Evry. The design of the experiment reported in this paper has been approved by the GATE-Lab Review Board for ethical standards in research, under the reference number 2022-14. The design and behavioral conjectures have been preregistered at AsPredicted (#124660 and #143285) (see Appendix H for a detailed discussion).</footnote>

<footnote>†Emlyon Business School, Université Lumière Lyon 2, Université Jean Monnet Saint-Etienne, GATE, CNRS, 69007, Lyon, France. Email: corgnet@em-lyon.com</footnote>

<footnote>‡GATE CNRS, Université Lumière Lyon 2, Université Jean-Monnet Saint-Etienne, Emlyon Business School, 69007, Lyon, France. Email: cornand@gate.cnrs.fr</footnote>

<footnote>§Université Paris Nanterre, EconomiX UMR 7235, F-92000 Nanterre, France. Email: pgandre@parisnanterre.fr</footnote>
# 1 Introduction

## 1.1 Macroeconomic disasters

The literature in macro-finance has long emphasized the crucial role of rare macroeconomic disasters in explaining various financial market puzzles (e.g., Barro, 2006; Gabaix, 2012; Gourio, 2012). Rare macroeconomic disasters are low probability events with drastic economic and financial consequences that are characterized by severe and persistent drops in real GDP (Barro, 2006). They may result from various factors, including wars, epidemics, and financial crises. A precise definition, proposed by Barro and Ursua, 2008, describes a macroeconomic disaster as a decline in real GDP per capita of more than 10% over at least one year.

The study of macroeconomic disasters has experienced renewed interest after the Covid pandemic, which led to various episodes characterized as disasters. The pandemic, which provoked one of the most dramatic stock market crashes in history (Mazur et al., 2021), is a vivid experience that will likely shape people's perception of economic risk and their subsequent financial investment decisions for years to come. Hanspal et al., 2021 show that people who were more negatively affected by the pandemic hold more pessimistic views about stock market recovery. Relatedly, Malmendier and Nagel, 2015 demonstrate that individuals who have experienced economic downturns throughout their life are less likely to take financial risk.

Rare and extreme events are often associated with two types of biases in risk perception (Kunreuther and Pauly, 2004; Hertwig and Erev, 2009): 1/ Underestimation of the risk preceding the disaster, and 2/ Overestimation of the disaster risk following the disaster. These misperceptions can be accounted for by the recency bias according to which people give excessive weight to more recent and vivid episodes (Tversky and Kahneman, 1973; Rabin, 2002; Barron and Yechiam, 2009; Plonsky et al., 2015).

Despite potential biases in the estimation of disaster risk, standard macro-finance models with rare macroeconomic disasters typically assume that agents either know the constant probability of occurrence of disasters (Barro, 2006 and Barro, 2009) or the exact distribution of a stochastic time-varying probability of disasters (Gabaix, 2012; Gourio, 2012; Wachter, 2013). However, because the micro-foundations of standard macro-finance models based on Full Information and Rational Ex-
pectations (FIRE) require extensive knowledge of disaster risk and have been clearly refuted in the literature in relation to other types of expectations (see, e.g., Coibion and Gorodnichenko, 2012), alternative paradigms have been developed. In models with information frictions, economic agents imperfectly perceive disaster risk because they are unable to attend to all the available information at a given moment (Collin-Dufresne et al., 2016b; Maćkowiak and Wiederholt, 2018; Ghaderi et al., 2022; Wachter and Zhu, 2024; Koulovatianos and Wieland, 2024). In behavioral models, agents typically make mistakes in forming their expectations due to cognitive biases, leading to an overweighting of recent and salient events (Bordalo et al., 2013; Collin-Dufresne et al., 2016a; Barberis et al., 2018; Bordalo et al., 2020; Bordalo et al., 2022).

Ultimately, determining which class of models best explains how individuals perceive macroeconomic disaster risk is an empirical question that requires the elicitation of disaster risk perceptions. However, the available data on the perception of macroeconomic disaster risk is scarce. Several papers have attempted to infer investors' disaster risk perceptions using option data or textual analysis of press articles (e.g., Manela and Moreira, 2017; Gao et al., 2018), but they only provide aggregate measures and therefore cannot account for individual heterogeneity. As a result, they cannot be used to evaluate how economic agents respond to information related to disaster risk. Relying on surveys of U.S. investors, Goetzmann et al., 2024 assess subjective beliefs about stock market crashes, while Giglio et al., 2021 evaluate the perceived probability of large declines in stock market returns and short-term economic activity. Menkhoff, 2025 measures the macroeconomic tail risk expectations of German firms and shows that they negatively affect firms' investment. Beutel and Stockerl, 2025 elicit German households' subjective probability of a financial crisis, a new pandemic, a China-Taiwan conflict, an energy crisis or a cyber attack in the next three years in the Bundesbank Survey of Consumer Expectations.

However, these economic events are less extreme than those that are the focus of the theoretical literature on macroeconomic disasters.<fnref n="1" /> At this point, it seems critical to collect data on how economic agents evaluate macroeconomic disaster risk and make subsequent risky financial investment decisions in order to directly test the assumptions of competing models in the macro-finance

<footnote n="1">¹In Giglio et al., 2021, GDP disasters are defined as episodes in which GDP growth is less than zero on average over the next three years. In Menkhoff, 2025, they are defined as annual drops in GDP of at least 5%.</footnote>
literature and guide future modeling and macroeconomic policies.

## 1.2 Perception of macroeconomic disaster risk

Therefore, our first research question (RQ1) asks whether economic agents form accurate beliefs of the frequency of macroeconomic disasters (see Figure 1). To that end, we conducted an online incentivized survey experiment that elicits agents’ beliefs of the historical frequency of macroeconomic disasters, defined as the number of disaster episodes relative to total observations. This frequency was calibrated using the historical dataset from Barro and Ursua, 2008, which we updated to 2021, resulting in an estimated frequency of 3%. Additionally, we assess agents’ willingness to invest in a risky asset whose expected returns explicitly depend on this past frequency. By eliciting beliefs about the frequency of macroeconomic disasters in historical data rather than about future disaster risk, we can directly quantify the over/underestimation of economic agents relative to an available past value. We can then also quantify the impact on financial investment decisions.

If the elicited beliefs are accurate, this would support standard FIRE macro-finance models. If they are not, one might first wonder whether our results are due to the insufficient financial knowledge of our survey participants. Indeed, individuals with more extensive financial knowledge may be more likely to compare their beliefs against historical frequencies, thereby exhibiting fewer biases (Tetlock and Gardner, 2015). These sophisticated economic agents are also more likely to make critical financial decisions, suggesting that collecting data from finance experts will ultimately increase the ecological validity of our study (Orne, 1962). Furthermore, the experimental literature has emphasized that finance experts may form different expectations and exhibit distinct behaviors compared to laypersons and students (e.g., Haigh and List, 2005; Cohn et al., 2014; Cohn et al., 2015; Kirchler et al., 2018). We thus conducted our survey with participants from two samples varying in their degree of financial expertise: a sample of 346 finance experts from France, and a sample of 590 participants recruited from the general French population via Prolific.
1.3 Information and macroeconomic disaster frequency

Any evidence that finance experts do not provide accurate perceptions of the historical frequency
of macroeconomic disasters would be at odds with FIRE models. Yet, alternative models empha-
size two possible explanations: imperfect information and behavioral biases. To distinguish be-
tween them, we conducted information treatments, allowing us to causally test whether economic
agents incorporate new information rationally, which addresses our second research question (see
RQ2 in Figure 1). More precisely, we conducted three information treatments using a between-
subject design in which participants were randomly assigned one of the information treatments.
All three information treatments elicited participants’ predictions of disaster frequency twice, with
relevant information being given only before their second perception elicitation.

To address the identification issues encountered when studying information provision using archival
data, we combined the methodology of randomized information experiments (Roth and Wohlfart,
2020; Beutel et al., 2021; Haaland et al., 2023; Fuster and Zafar, 2023; Stantcheva, 2023) with that of
incentivized laboratory experiments (Smith, 1982; Bardsley et al., 2010). We used incentives to en-
courage participants to provide thoughtful answers by compensating their cognitive effort, which
can improve the precision of responses (Smith, 1991; Read, 2005).<fnref n="2" /> Incentives also enhance the
realism of decisions by reflecting the financial consequences faced by investors and are essential
for recruiting and motivating finance experts (Kirchler et al., 2018). Additionally, they may help
mitigate experimenter demand effects and other subjective biases (Bardsley et al., 2010; Haaland
et al., 2023).

In the Small Sample treatment with 116 experts and 120 laypersons, we informed participants
that, when randomly selecting 100 cases from our historical database, 3 were identified as macroe-

<footnote n="2">²The macroeconomic literature remains divided on whether incentivizing survey responses improves the accuracy of expectations. Some studies raise concerns about the reliability of self-reported expectations, suggesting that respondents, especially those with lower education levels, may not provide well-considered answers without appropriate incentives (Inoue et al., 2009 and Keane and Runkle, 1990). Keane and Runkle, 1990 do find that professional forecasters tend to report accurate beliefs, likely driven by reputational stakes. However, other contributions focused on households' forecasts (Armantier et al., 2015, Roth and Wohlfart, 2020, and Andre et al., 2022) find little or no impact of incentives on reported expectations. Armantier et al., 2015 observe meaningful correlations between unincentivized expectations and incentivized behavior for financially literate individuals. Nevertheless, more recently, the work of Drobot et al., 2025 represents a substantial shift by embedding marginal incentives within a standard consumer expectations survey. Their findings suggest that such incentives reduce upward bias, decrease disagreement across responses, close the gender gap, increase effort, and improve responsiveness to information, highlighting the potential of incentive-based designs in household expectation surveys.</footnote>
conomic disasters.<fnref n="3" /> Thus, the value of the information signal was equal to the historical frequency
in the database.

The Large Sample treatment (106 experts, 121 laypersons) was the same as the previous treatment except that we provided participants with a more precise signal. We informed them that 30 out of 1,000 randomly selected cases were identified as macroeconomic disasters. The purpose of this treatment was to alleviate the recency bias (Tversky and Kahneman, 1973; Rabin, 2002; Barron and Yechiam, 2009; Plonsky et al., 2015) by increasing the weight assigned to the signal while reducing the weight assigned to a person's prior perceived frequency, which is likely to be heavily influenced by her latest experience.

The Vivid Sample treatment (124 experts, 117 laypersons) was the same as the Small Sample treatment, except that we provided participants with a brief description of 3 macroeconomic disasters from a sample of 100, deliberately focusing on recent and relatable events. The cases were the Greek debt crisis (2008-2013) and the UK and Spain pandemic episodes (2020). This treatment maintained the precision of the signal the same as in the Small Sample treatment. The aim of the treatment was to leverage the availability heuristic (Tversky and Kahneman, 1973; Pachur et al., 2012). The latter suggests that concrete and recent cases are more likely to stimulate people's episodic memory and, as a result, are easier to recall than quantitative signals, thus receiving higher weight in risk assessments.

Because information treatments were applied to the second prediction of the frequency of macroeconomic disasters, we assessed their impact through a within-subject comparison of participants' first and second prediction of disaster risk and investment decisions. To complement this analysis, we also conducted a Control treatment (117 laypersons) in which no information was provided before the second prediction of disaster frequency. The Control treatment allowed us to assess the impact of information treatments on disaster perception and investment decisions using a between-subject analysis.

<footnote n="3">³Our updated version of the dataset used in Barro and Ursua, 2008 includes a total of 6,009 observations.</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: Research questions and modeling implications</figcaption></figure>

## 1.4 Overview of the results

Our findings offer a clear response to our first research question: economic agents, regardless of the population, substantially overestimated the historical frequency of macroeconomic disasters. Laypersons predicted the historical frequency of a macroeconomic disaster at 21.8% on average compared to 3% in the updated dataset (Barro and Ursua, 2008).

As expected, this overestimation was less pronounced among finance experts although their average perception was as high as 15.0%. Regardless of the population, the overestimation of the historical frequency of a disaster, which was used to calculate the downside risk associated with our investment task (à la Gneezy and Potters, 1997), led to lower levels of investment, thereby reducing participants' earnings in the experiment.

Given the negative answer to our first research question, we then wondered how information provision could alleviate investors' misperceptions of macroeconomic disaster risk and enhance investment levels. In line with information models, we find that both finance experts and laypersons substantially revised their predictions downward consistently with the information provided. Across all information treatments, laypersons (experts) decreased their prediction of the historical frequency of disasters from 21.8% to 14.9% (from 15.0% to 12.2%). Interestingly, in line with information models that assume that economic agents are Bayesian, laypersons whose prior prediction was based on less extensive information than experts, revised their predictions more extensively. Not only did information treatments lead to more accurate predictions of disaster frequency, but they also implied a convergence between laypersons and expert predictions. It follows that the foregone earnings associated with an insufficient level of investment in the risky asset were re-
duced by the information treatments, as laypersons (experts) increased their share of investment in the risky asset from 39.5% to 45.0% (from 53.2% to 56.9%) following the information treatments. However, at odds with macroeconomic models featuring imperfect information (see Figure 1), increasing the precision of the information did not improve the accuracy of the predictions, as no significant differences were observed between the Small Sample and Large Sample treatments, regardless of the population.

In sum, information led to a revision of the predicted frequency of disasters in the right direction, but more precise information did not have the intended effect. This last result suggests that behavioral biases may also impact people's perception of macroeconomic disaster risk. The Vivid Sample treatment offers direct evidence of these potential behavioral biases. In this treatment, the information that only 3 out of 100 cases in the database were disasters, typically leading to a downward revision of disaster frequency, could be partly offset by the vivid description of these three cases, which would likely exacerbate disaster risk perception. This offsetting effect of the vividness of the information is consistent with the fact that laypersons revised their predicted disaster frequency to a lesser extent in the Vivid Sample treatment (from 22.6% to 17.6%) compared to the Small Sample treatment (from 22.5% to 15.2%). By contrast, experts seemed to be unaffected by the Vivid Sample treatment, as their downward revision was the same in both treatments.

Our study advances our understanding of effective communication policies by offering practical guidelines for financial authorities and policymakers. In particular, our findings emphasize that providing quantitative information about macroeconomic disaster risk can have a substantial impact on beliefs and investment levels, especially for laypersons. However, we show that the effectiveness of this strategy is constrained by people's lack of response, regardless of their financial expertise, to the precision of the information. Yet, our findings also suggest a remedy by showing that, at least in the case of laypersons, increasing the vividness of the information may have a greater impact than increasing its precision.

In sum, we demonstrate that providing historical information about macroeconomic disasters significantly impacts beliefs about disaster risk and investment levels. However, the effectiveness of this information policy critically depends on the target population and the manner in which the information is presented.
## 2 Hypotheses

In line with the two research questions shown in Figure 1, we propose three hypotheses, each corresponding to one type of model (FIRE, information, and behavioral).

Since Muth, 1961, the standard paradigm in macro-finance theory has been the FIRE hypothesis, according to which agents, whatever their individual characteristics, form optimal forecasts based on a full information set. Under FIRE, expectations are thus unbiased. Agents also act in an optimal manner, conditional on their forecast. Applied to our context with macroeconomic disasters, and focusing on the prediction of their past frequency (and not on forward-looking risk), we can derive the following hypothesis:

**Hypothesis 1. FIRE models**

**Hypothesis 1a. (Prediction)** Economic agents will accurately predict the historical frequency of macroeconomic disasters.

**Hypothesis 1b. (Investment)** Economic agents with higher disaster predictions will invest less in the risky asset, controlling for their risk attitudes.

**Hypothesis 1c. (Experts vs. laypersons)** Finance experts and laypersons will not differ in their disaster predictions, confidence intervals and investment levels.

Hypothesis 1a, which posits that the predictions of macroeconomic disaster frequency elicited in our survey experiment should be generally accurate, provides a basic check for FIRE models. However, given the growing body of evidence showing that people, including professionals, form inaccurate expectations about macroeconomic variables (Coibion and Gorodnichenko, 2015; Angeletos et al., 2021; Bordalo et al., 2022; Afrouzi et al., 2023), we consider alternative testable implications based on models that can account for incorrect perceptions of the frequency of macroeconomic disasters.

In models with information frictions, economic agents predict disaster risk imperfectly due to rational inattention (Maćkowiak and Wiederholt, 2018), imperfect information on disaster realizations (Ghaderi et al., 2022; Koulovatianos and Wieland, 2024) or uncertainty on the disaster risk
data-generating process (Collin-Dufresne et al., 2016b; Wachter and Zhu, 2024) while new information is rationally incorporated. In these models, agents update their prior belief in a Bayesian way, forming posteriors by including new information, weighted according to its relative precision. Economic agents then behave optimally conditional on their imperfect information set. Assuming that laypersons are less informed or have less financial knowledge than experts, models with information frictions imply the following hypothesis:

**Hypothesis 2.** *Imperfect information models*

**Hypothesis 2a. (Direction)** Economic agents will update their disaster predictions and their investment levels in the direction of the gap between the information signal and their prior prediction. Following the provision of information, they will become more confident in their predictions.

**Hypothesis 2b. (Precision)** The magnitude of the updates in disaster predictions, confidence intervals and investment levels will be more pronounced in the Large Sample treatment than in the Small Sample treatment.

**Hypothesis 2c. (Experts vs. laypersons)** The effect of information and its precision will be more pronounced for laypersons than for experts.

In behavioral models, agents form erroneous beliefs about the frequency of macroeconomic disasters conditional on their information set (Chen et al., 2010; Chen et al., 2012). These mistakes, which result from cognitive biases such as recency (Collin-Dufresne et al., 2016a; Barberis et al., 2018) and salience (Bordalo et al., 2013; Bordalo et al., 2022; Bordalo et al., 2020), have been integrated into recent macro-finance frameworks. These cognitive biases imply that agents tend to overweight representative events in reacting to information (see also Bordalo et al., 2018; Gennaioli and Shleifer, 2018; Bordalo et al., 2020).

The provision of salient information on macroeconomic disasters is therefore less likely to lead agents to revise their frequency of disaster downward. This bias towards salient information is expected to be more pronounced for laypersons than experts for two reasons. First, laypersons are expected to hold less accurate prior beliefs about disaster risk, which makes them more likely to be influenced by both the quantity and precision of information (Hypothesis 2c), as well as its
salience (Hypothesis 3b). Second, finance professionals are expected to possess higher cognitive skills, such as cognitive reflection (Frederick, 2005), than laypersons, which predicts an individual's capacity to avoid decision heuristics such as those related to salience (e.g., Cokely and Kelley, 2009; Oechssler et al., 2009; Campitelli and Labollita, 2010; Toplak et al., 2011). Below, we state our third hypothesis regarding the implications of behavioral models on disaster risk predictions.

**Hypothesis 3.** *Behavioral models*

**Hypothesis 3a.** (*Vividness*) In the Vivid Sample treatment, economic agents will be less likely to revise their macroeconomic disaster predictions downward and their investment levels upward than in the Small Sample and Large Sample treatments.

**Hypothesis 3b.** (*Experts vs. laypersons*) *The effect of vividness will be more pronounced for laypersons than for experts.*

**3 Randomized incentivized information experiment**

We conducted an online, incentivized, randomized information provision experiment with finance experts and laypersons. The experiment consists of three stages and six decisions, as shown in Figure 2.

<figure><img src="image_3.png" /><figcaption>Figure 2: Sequence of the experiment</figcaption></figure>

We used incentives to induce participants to expand effort to read instructions carefully and provide thoughtful answers, as research suggests that this approach yields more precise responses (Smith, 1991; Read, 2005). We also wanted to make decisions consequential since, in the field, finance experts and laypersons directly bear the financial consequences of their (potentially biased) predictions and subsequent investment decisions. Incentivizing the experiment also enables us
to quantify the monetary impact of biased financial decisions on our participants. Furthermore,
substantial incentives, including both fixed pay and performance bonuses, are often necessary to
recruit and motivate finance experts for experiments (Kirchler et al., 2018). Finally, incentives can
be used to mitigate demand effects, whereby respondents alter their decisions based on their per-
ception of the experimenter’s goals (Haaland et al., 2023). More generally, subjective factors, such
as the framing of instructions or the presence of peers, that influence individual decisions might
be attenuated by the use of monetary incentives (Bardsley et al., 2010).

Laypersons were paid a participation fee of 5 euros along with a performance bonus that was paid to 1 out of 10 participants. This bonus was equal to the gains obtained by the selected participant in one of the six decisions selected at random. Paying only one of the six decisions allowed us to eliminate hedging issues (Armantier and Treich, 2013) with a maximum bonus payment equal to 20 euros. In addition to the participation fee and the bonus, to enhance participation, we also randomly drew one participant for a 300 euros lottery. For finance experts, we used the same payoff structure but, in line with the standards in the literature (e.g., Huber et al., 2021), all rewards were multiplied by 3 (i.e., the participation fee was 15 euros, the maximum performance bonus was 60 euros, and the lottery prize was 900 euros).<fnref n="4" />

**3.1 Stage 1 (prior predictions)**

In the first stage, participants make three decisions.

**Decision 1: Disaster frequency (point prediction)**

The first decision is to predict the historical frequency of macroeconomic disasters. To ensure common understanding, we first gave participants the following definition of macroeconomic disasters inspired by Barro and Ursua, 2008: "A macroeconomic disaster is defined as a cumulative drop in GDP per capita, net of price trends, of more than 10% over a period of at least one year (from peak to trough) in a given country."

Participants were then told that the historical frequency was calculated using a real historical

<footnote n="4">⁴The magnitude of incentives was substantial. In our experiment, the expected earnings for a financial professional was about 20 euros for an average of 17 minutes of work, that is an hourly wage of 71 euros, which is larger than the industry estimate. Indeed, the annual average wage (before income tax) of a financial professional in France is about 57,000 euros, yielding an average hourly income of 35 euros (see https://fr.talent.com/salary).</footnote>
dataset. This dataset was built on the one used in Barro and Ursua, 2008, which we updated through 2021. It reports historical annual GDP per capita data, adjusted for price changes, collected from 42 countries, including advanced, emerging, and developing economies. Each observation represents the value of the real annual GDP per capita in a given country and a given year. The data goes back to 1870 for most countries and at least to 1911 for others, and extends to 2021 (except for Taiwan and Venezuela, for which it extends to 2019). This amounts to a total of 6,009 data points. We thus informed participants that the historical frequency (in %) of a macroeconomic disaster in the historical dataset is calculated using the following formula:

$$ \frac{\text{Number of disasters in the historical dataset}}{\text{Total number of data points in the historical dataset}} \times 100.5 $$

Based on this calculation, the historical frequency of a macroeconomic disaster is 3%. To predict this historical frequency, participants were given the list of countries and years considered in the dataset (see Appendix B) but not the dataset itself. Following standard practices in experimental economics, we incentivized the disaster prediction decision (Smith, 1982; Bardsley et al., 2010) using a Quadratic Scoring Rule (QSR) (Brier, 1950; Selten, 1998), which is commonly used in the literature (Schotter and Trevino, 2014).

The payoffs in euros were thus given by the following formula: $C_1$ – (prediction of frequency – exact frequency)$^2$, with $C_1$ = 60 for finance experts and 20 for laypersons. The instructions included an explanation accompanying the formula, making it clear to the participants that their potential payoff would increase the closer their answer was to the actual frequency of macroeconomic disasters in the historical dataset.

Like other Proper Scoring Rules, the Quadratic Scoring Rule (QSR) is incentive compatible only for risk-neutral expected utility maximizers (see, e.g., Armantier and Treich, 2013). Under QSR, risk-averse individuals tend to distort their reported beliefs. This issue can be alleviated by using a Binarized Scoring Rule (BSR) (Hossain and Okui, 2013). However, the benefits of BSR come at the cost of being more difficult to understand than QSR. Since our experiments were conducted

<footnote n="5">⁵Note that this formula does not, strictly speaking, calculate a frequency, since a disaster can last several years, but rather the probability of entering into a disaster, as defined in Barro, 2006. Participants were given this formula and the total number of data points in the historical dataset (6,009), so that what they had to predict was simply the number of disasters in the sample, regardless of their duration.</footnote>
online, making it impossible to enforce the time participants actually spent on each question, we opted to use QSR, which is the simpler rule.

Furthermore, we show that our main results regarding the overestimation of disaster frequency and the differences across samples continue to hold when we restrict our analysis to participants who are neither substantially risk-averse nor risk-seeking (see Appendix G.1). It follows that our results are unlikely to be explained by differences in risk attitudes across samples, which could have influenced our elicitation of disaster risk differently for finance experts and non-experts.

Our results are also unlikely to be explained by participants’ misunderstanding of the elicitation procedure. We show that even non-experts who achieved the highest possible scores on both the financial literacy and numeracy tests substantially overestimated disaster risk, and that participants who achieved the maximum score on the financial literacy test in both groups also overestimated disaster risk, with the overestimation being significantly more pronounced among non-experts (see Appendix G.2).

**Decision 2: Disaster frequency (confidence interval)**

In the second decision, we assessed participants’ confidence in their point prediction, allowing us to test Hypothesis 2a and 2b. To that end, we asked participants to provide a confidence interval for the historical frequency of disasters. This task was rewarded only if the chosen confidence interval contained the historical frequency of a macroeconomic disaster (i.e., 3%), and the magnitude of the reward decreased with the width of the chosen interval following Schlag and van der Weele, 2015. The payoffs in euros were thus calculated according to the following formula: $(100 - \text{width of chosen interval}) \times C_2$, with $C_2 = 0.6$ for finance experts and 0.2 for laypersons.

**Decision 3: Investment**

In the third decision, participants were asked to make an investment decision by splitting an endowment between a risky asset and a risk-free asset. This allowed us to evaluate the link between expectations and risky financial decisions. Specifically, participants allocated €100,000 between a risk-free asset with a guaranteed 1% return and a risky asset. The return of the risky asset depended on the occurrence of a macroeconomic disaster. For each participant, a random observation
was selected from the historical dataset.<fnref n="6" /> If the selected observation was a macroeconomic disaster, the return on the risky asset was −30%; otherwise, it was +10%. The gains of the participants for this decision were then calculated as the final value of the portfolio divided by 2,000.

We calibrated the returns on risk-free and risky assets using historical data and matched the yearly value of the equity premium (Barro and Ursua, 2008). Given the chosen parameters, the risky investment is especially appealing. For example, assuming that investors maximize a power utility function ($u(x) = x^{1-\gamma}$), a parametric specification that has been validated in numerous empirical studies (see e.g., Gonzalez and Wu, 1999; Stott, 2006), and correctly predict the historical frequency of disasters at 3%, they should invest all their money in the risky asset unless they exhibit unrealistic levels of relative risk-aversion ($\gamma = 10$) that are typically not observed in empirical studies (see Table 1 below).<fnref n="7" />

<table><caption>Table 1: Optimal investment levels across risk attitudes.</caption><thead><tr><td>Risk attitudes</td><td>Coefficient of relative risk aversion</td><td>Optimal share of risky asset</td></tr></thead><tbody><tr><td>Risk-neutrality</td><td>0</td><td>100%</td></tr><tr><td>Slight risk-aversion</td><td>0.5</td><td>100%</td></tr><tr><td>Moderate risk-aversion</td><td>2</td><td>100%</td></tr><tr><td>High risk-aversion</td><td>4</td><td>100%</td></tr><tr><td>Extremely high risk-aversion</td><td>10</td><td>56%</td></tr></tbody></table>

## 3.2 Stage 2 (treatments)

Once the three decisions have been made, the treatments are implemented. Participants who took part in sessions from April to November 2023 were randomly assigned to one of the three information treatments where they were provided additional information on the historical dataset of macroeconomic disasters.

<footnote n="6">⁶We explained to the participants that a given disaster is counted only once in the dataset, regardless of its duration, so that the probability of drawing a disaster is equal to the calculated historical frequency.</footnote>

<footnote n="7">⁷Most studies report $\gamma$ estimates between 0 and 2 (see e.g., Hansen and Singleton, 1983; Tversky and Kahneman, 1992; Wu and Gonzalez, 1996; Gonzalez and Wu, 1999; Abdellaoui, 2000; Holt and Laury, 2002; Abdellaoui et al., 2005. Abdellaoui et al., 2008; Chetty, 2006; Booij and van de Kuilen, 2009; Booij et al., 2009; Bruhin et al., 2010). Coefficients of relative risk aversion above 10 are considered unrealistic and Mehra and Prescott, 1985 consider this value as the upper bound of their admissible region in attempting to explain the equity premium puzzle, whereas Guvenen, 2006 considers a median value of 3 with an upper bound of 7.</footnote>
In the Small Sample treatment, participants were informed that 100 out of 6,009 observations were randomly selected from the dataset and that 3 disasters had occurred.

In the Large Sample treatment, participants were informed that 1,000 out of 6,009 observations were randomly selected from the dataset and that 30 disasters had occurred. This treatment increased the precision of the information provided to participants compared to the Small Sample treatment.

In the Vivid Sample treatment, participants received the same information as in the Small Sample treatment, with the addition of specific country names, dates, and brief statements about the event that caused the disaster. This part of the instructions thus reads as follows: "The first corresponds to the 2008-2013 period in Greece (consequence of the financial crisis), the second corresponds to 2020 in the UK (consequence of the Covid pandemics) and the third corresponds to 2020 in Spain (also consequences of the Covid pandemics)." Because the selected episodes were recent and relatable to French participants, they were likely to be salient and trigger negative feelings, which, according to the availability heuristic, would lead to an overestimation of disaster risk.

In June 2024, we also conducted a Control treatment in which no information was released in Stage 2.

## 3.3 Stage 3 (posterior predictions)

In Stage 3, we replicated Stage 1 and asked participants to report a second prediction of the historical frequency of disasters (Decision 4) along with a confidence interval (Decision 5) and an investment allocation (Decision 6).

To limit the effect of numerical anchoring (Furnham and Boo, 2011) and response inertia that could induce participants to stick to their prior predictions, we asked participants two unrelated questions about economic growth and inflation forecasts over the next 12 months at the very beginning of Stage 3.<fnref n="8" />

At the end of the experiment, participants were asked demographic questions about their gender, age, level of education and field of study, and financial literacy. For finance experts, we also asked them about their industry, profession, and the type of markets in which they operate. Instead, for

<footnote n="8">⁸See Appendix D.2 for details.</footnote>
laypersons, we added questions that assessed their numerical literacy.

## 3.4 Procedures

The experiment was programmed using Limesurvey.<fnref n="9" /> The experiment with finance experts was conducted online via the 'Laboratoire d'Expériences en Ligne' (LEEL) in Paris. Finance experts were contacted through French associations of finance professionals (the Association Française de Gestion Financière (AFG), the Société Française des Analystes Financiers (SFAF), the Association Française des Investisseurs Institutionnels (AF2i) and the Chartered Financial Analysts (CFA) France), through individual financial institutions and French administrations involved in financial and economic issues, and through associations of alumni of Masters in Finance at the Universities of Lyon and Paris Nanterre, and at Emlyon Business School. The experiment with laypersons was run on Prolific with a French population. We recruited a total of 346 French finance experts and 590 non-expert participants (laypersons) from France.

While almost all sessions were conducted between April and November 2023, an additional Control treatment along with a Small Sample treatment, with 117 and 115 participants, respectively, were conducted on Prolific in June 2024, involving laypersons from France. We conducted an additional Small Sample treatment to determine whether the disaster predictions of participants surveyed on Prolific in June 2024 were similar to those surveyed in 2023. Since this was indeed the case, we were reassured that the Control treatment collected in June 2024 could be reliably compared with the information treatments collected in 2023. The experiments lasted on average 17 minutes for finance experts and 19 minutes for laypersons. When we exclude the time spent on the post-experimental survey that differed between the two groups, the experiment lasted on average 12 minutes for both groups.

Table 2 shows an overview of the experimental sessions, and translated instructions are provided in Appendix A.<fnref n="10" />

<footnote n="9">⁹The online survey is available at: https://s2ch-experiences.huma-num.fr/limesurvey/index.php/545367?lang=fr.</footnote>

<footnote n="10">¹⁰The number of participants is not perfectly equal between treatment arms because, although the URLs leading to the experiment were equally distributed across treatments, some participants did not complete the experiment.</footnote>
<table><caption>Table 2: Overview of experimental sessions.</caption><thead><tr><th>Treatment</th><th>Population</th><th>Date</th><th>Platform</th><th>Number of participants</th></tr></thead><tbody><tr><td rowspan="3">Small Sample</td><td>Finance experts</td><td>Between April and November 2023</td><td>Online via LEEL</td><td>116</td></tr><tr><td>Laypersons</td><td>June 2024</td><td>Prolific</td><td>120</td></tr><tr><td>Laypersons</td><td></td><td>Prolific</td><td>115</td></tr><tr><td rowspan="2">Large Sample</td><td>Finance experts</td><td>Between April and November 2023</td><td>Online via LEEL</td><td>106</td></tr><tr><td>Laypersons</td><td></td><td>Prolific</td><td>121</td></tr><tr><td rowspan="2">Vivid Sample</td><td>Finance experts</td><td>Between April and November 2023</td><td>Online via LEEL</td><td>124</td></tr><tr><td>Laypersons</td><td></td><td>Prolific</td><td>117</td></tr><tr><td>Control</td><td>Laypersons</td><td>June 2024</td><td>Prolific</td><td>117</td></tr></tbody></table>

Appendix C summarizes the balance tables between treatments (Table A.C.1.1), compares the sociodemographic characteristics of our two samples with those of the general French population (Table A.C.2.1), and provides descriptive statistics before and after treatment (Table A.C.3.1).

As expected, the sample of finance experts includes fewer women than the sample of laypersons. In addition, finance experts are more educated, have higher financial literacy scores and risk appetites, are more likely to hold financial assets, and more likely to have studied economics and finance compared to laypersons. Overall, our sample of laypersons is younger and more educated than the general French population and has a higher average financial literacy score. This suggests that our study likely provides a lower bound for the differences between finance experts and laypersons.

# 4 Results

We test our hypotheses using the data collected in 2023 (n = 704) while using the newly collected data (n = 232, Control treatment and Small Sample treatment collected in 2024) for robustness checks (see Appendix D).

## 4.1 Hypothesis 1 (FIRE)

To evaluate Hypothesis 1, we use prior predictions decisions from Stage 1, which are unimpacted by information treatments.
4.1.1 Overestimation of disaster frequency

As shown in Figure 3, on average, participants, regardless of financial expertise, largely overesti-
mate the historical frequency of past macro disasters before information provision.

<figure><img src="image_4.png" /><figcaption>Figure 3: Average disaster prediction along with 95% confidence intervals (left panel) and kernel density (right panel) across populations.</figcaption></figure>

Laypersons perceive the historical frequency of a macroeconomic disaster at 21.8% on average
compared to an actual value of 3%. This overestimation is less pronounced among finance experts
although their average prediction is 15.0%. In both populations, the average prior prediction of
disaster frequency is significantly different from the actual historical frequency (Sign Rank Tests,
*p*-values < 0.001).

Looking at the distribution of predictions, we report that 84.6% (78.0%) of laypersons (experts) overestimate the frequency of a macroeconomic disaster, while only 3.6% (6.1%) provide predictions equal to the actual value. Even when allowing a margin of error of 1%, only 12.0% (20.8%) of laypersons (experts) provide an answer in the adequate interval [2%, 4%].

Table 3 presents the determinants of prior disaster predictions, showing that they decrease with financial literacy and increase with prior uncertainty (i.e., with the size of the confidence interval).
<table><caption>Table 3: Linear regressions of the determinants of prior disaster predictions.</caption><thead><tr><th></th><th>(1)<br>Disaster prediction<br>(Stage 1)</th><th>(2)<br>Disaster prediction<br>(Stage 1)</th></tr></thead><tbody><tr><td>Confidence interval (Stage 1)</td><td>0.584***<br>(0.074)</td><td>0.569***<br>(0.073)</td></tr><tr><td>Finance experts Dummy</td><td>-0.277<br>(1.494)</td><td>1.695<br>(1.812)</td></tr><tr><td>Financial literacy</td><td>-3.803***<br>(0.899)</td><td>-3.517***<br>(0.929)</td></tr><tr><td>Age</td><td></td><td>-0.085<br>(0.052)</td></tr><tr><td>Male Dummy</td><td></td><td>-0.721<br>(1.388)</td></tr><tr><td>Response time</td><td></td><td>-0.002<br>(0.001)</td></tr><tr><td>Student Dummy</td><td></td><td>2.520<br>(2.454)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>0.081<br>(1.740)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>0.357<br>(1.729)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>-3.418*<br>(1.748)</td></tr><tr><td>Risk appetite</td><td></td><td>0.077<br>(0.359)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>0.683<br>(1.664)</td></tr><tr><td>Constant</td><td>24.958***<br>(4.079)</td><td>28.765***<br>(5.095)</td></tr><tr><td>R<sup>2</sup></td><td>0.32</td><td>0.33</td></tr><tr><td>N</td><td>704</td><td>704</td></tr></tbody></table>

*Notes*: Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** *p* < 0.01, ** *p* < 0.05, * *p* < 0.1)
The overestimation of macroeconomic disaster risk is consistent with Giglio et al., 2021 and Goetzmann et al., 2024 who show that US investors assign a high probability to stock crashes or large GDP declines in the post-Covid context. Overall, our findings reject Hypothesis 1a according to which economic agents hold accurate predictions of disaster risk.

### 4.1.2 Underinvestment in the risky asset

We proceed by testing Hypothesis 1b according to which higher predictions of disaster risk will reduce the share allocated to the risky asset in the investment task (Risky share, henceforth). In Table 4, we find that, as expected, a higher disaster prediction is negatively associated with the share invested in the risky asset, whose returns depend on the potential realization of a disaster. This relationship holds even when controlling for demographics and risk attitudes (see regression (2)). We also note that a larger confidence interval, which captures a greater level of uncertainty regarding one's disaster prediction, leads to lower risky investments. Unsurprisingly, risk appetite is positively associated with risky investments.

Overall, the average level of investment in the risky asset is only 39.5% for laypersons and 53.2% for finance experts, which is inconsistent with the optimal levels of investment derived when investors know the actual frequency of macroeconomic disasters (see Table 1).

The reason for this underinvestment is likely due to the overestimation of the frequency of a disaster, which is used to calculate the downside risk associated with our investment task. Given their overestimation of disaster risk, laypersons (experts) substantially underestimate the expected return of the risky asset at 1.3% (4.0%) compared to an actual value of 8.8%, thus investing insufficiently in the risky asset. It follows that, given a prediction of 21.8% (15.0%) for macroeconomic disaster risk by laypersons (experts), investing 39.5% (53.2%) in the risky asset is actually compatible with a plausible level of relative risk aversion of 0.28 (2.13). The overestimation of disaster risk by laypersons (experts) thus produces a portfolio that underinvests in the risky asset, resulting in an expected return substantially lower (4.1% (5.4%)) than the optimal portfolio (8.8%).
<table><caption>Table 4: Linear regressions of the determinants of risky investments.</caption><thead><tr><th></th><th>(1)<br>Risky share<br>(Stage 1)</th><th>(2)<br>Risky share<br>(Stage 1)</th></tr></thead><tbody><tr><td>Disaster prediction (Stage 1)</td><td>-0.170***<br>(0.061)</td><td>-0.172***<br>(0.055)</td></tr><tr><td>Confidence interval (Stage 1)</td><td>-0.214**<br>(0.084)</td><td>-0.175**<br>(0.078)</td></tr><tr><td>Finance experts Dummy</td><td>11.789***<br>(2.492)</td><td>6.648*<br>(3.464)</td></tr><tr><td>Financial literacy</td><td>-0.192<br>(1.099)</td><td>-2.953***<br>(1.121)</td></tr><tr><td>Age</td><td></td><td>0.022<br>(0.113)</td></tr><tr><td>Male Dummy</td><td></td><td>0.250<br>(2.255)</td></tr><tr><td>Response time</td><td></td><td>-0.003*<br>(0.002)</td></tr><tr><td>Student Dummy</td><td></td><td>-3.108<br>(2.895)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>-4.996<br>(3.313)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>1.556<br>(2.953)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>4.174*<br>(2.372)</td></tr><tr><td>Risk appetite</td><td></td><td>5.046***<br>(0.510)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>3.847<br>(2.358)</td></tr><tr><td>Constant</td><td>47.695***<br>(4.547)</td><td>30.467***<br>(6.012)</td></tr><tr><td>R<sup>2</sup></td><td>0.09</td><td>0.24</td></tr><tr><td>N</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
If instead of recognizing the role of misperception of disaster risk, we followed FIRE models and considered that economic agents accurately perceived disaster risk, we would not be able to explain participants' investment decisions unless we assumed unrealistic coefficients of relative risk aversion of around 15 (for laypersons) and 10 (for experts).

Therefore, we find that laypersons, as well as finance experts, overestimate the frequency of disasters and underinvest in the risky asset as a result. As shown in Appendix D.1, these findings continue to hold with the data collected with laypersons in June 2024 for the Control and additional Small Sample treatment.

### 4.1.3 Differences in disaster predictions and investments between laypersons and experts

At odds with Hypothesis 1c, the prediction of finance experts is more accurate than that of laypersons (15.0% vs. 21.8%, Rank Sum Test, *p*-value < 0.001). Furthermore, finance experts provide a narrower confidence interval than laypersons (13.5% vs. 17.9%, Rank Sum Test, *p*-value < 0.001), which likely reflects their more extensive financial knowledge.

As evidenced in Table 3, the more accurate disaster predictions of experts compared to laypersons can be entirely explained by differences in financial literacy. This is the case because in regression (1), the effect of the variable 'Financial literacy' is negative and significant, showing that more financially literate respondents provided lower disaster predictions, while the coefficient associated with the variable 'Finance experts Dummy' is not significant. By contrast, disaster predictions do not appear to be affected by the other individual controls, which are non-significant (see regression (2)).

Regarding risky investments, we also find significant differences across populations as experts invested substantially more than laypersons (53.2% vs. 39.5%, Rank Sum Test, *p*-value < 0.001), which is incompatible with Hypothesis 1c. However, these differences are mitigated after controlling for risk appetite, confidence in one's own prediction, and financial literacy (see coefficient on the variable 'Finance experts Dummy' in regression (2) of Table 4, which becomes non-significant). In the next section, we test the impact of information provision on disaster risk predictions (Hypothesis 2) as a way to potentially alleviate the overestimation of disaster risk and boost risky
## 4.2 Hypothesis 2 (Imperfect information models)

### 4.2.1 Updating disaster predictions in the right direction

To evaluate Hypothesis 2, we need to assess the impact of information treatments so that we will use data from both Stage 1 (prior predictions) and Stage 3 (posterior predictions). Then, first, to test Hypothesis 2a, we need to assess the extent to which Stage 1 disaster predictions are updated in Stage 3 in the direction of the information provided in Stage 2.

In Figure 4, we observe that the distribution of posterior predictions shifted to the left relative to the distribution of prior predictions, with a higher number of responses concentrated around the actual historical value of 3% for both populations. The share of laypersons (experts) who overestimate the frequency of a macroeconomic disaster significantly decreased from 84.6% to 69.0% (from 78.0% to 66.2%) (Proportion Tests, *p*-values < 0.001). These results support Hypothesis 2a.

In line with Hypothesis 2a, Table 5 shows that information provision leads participants to update their prediction in the direction of the gap between the information signal provided in Stage 3 (3%) and their prior prediction in Stage 1. Indeed, the coefficient for ‘Gap’ is positive and significant in explaining the update in disaster predictions. This means that the update goes in the right direction as participants who had overestimated (underestimated) disaster risk updated their prediction downward (upward), supporting Hypothesis 2a.

In Appendix D.2, we alleviate concerns that the update in the direction of the signal provided could reflect numerical anchoring related to the quantitative nature of the signal. We exploit the additional experiment we conducted in 2024 in Prolific in which we included a control group that received no information in Stage 2 and a group that received the Small Sample treatment. We show that post-treatment quantitative forecasts unrelated to the signal (forecasts of economic growth and inflation over the next 12 months) do not significantly differ between the two groups. This implies that participants respond to information if they find it meaningful for the prediction they have to make and not because they stick to a given numerical value.

In line with Hypothesis 2a, the results in Table 6 show that an update in the prediction of disas-
<table><caption>Table 5: Linear regressions of the update of disaster predictions as a function of the gap between information and one's prior prediction.</caption><thead><tr><th></th><th>(1)<br>Update Disaster prediction<br>(Stage 3)</th><th>(2)<br>Update Disaster prediction<br>(Stage 3)</th></tr></thead><tbody><tr><td>Gap</td><td>0.377***<br>(0.057)</td><td>0.383***<br>(0.056)</td></tr><tr><td>Confidence interval (Stage 1)</td><td>0.088<br>(0.053)</td><td>0.084<br>(0.052)</td></tr><tr><td>Finance experts Dummy</td><td>3.468***<br>(1.085)</td><td>2.830*<br>(1.500)</td></tr><tr><td>Financial literacy</td><td>-1.553**<br>(0.647)</td><td>-1.507**<br>(0.670)</td></tr><tr><td>Large Sample Dummy</td><td>-1.051<br>(1.036)</td><td>-0.951<br>(1.024)</td></tr><tr><td>Vivid Sample Dummy</td><td>1.088<br>(1.088)</td><td>1.434<br>(1.085)</td></tr><tr><td>Age</td><td></td><td>-0.019<br>(0.044)</td></tr><tr><td>Male Dummy</td><td></td><td>-2.890**<br>(1.155)</td></tr><tr><td>Response time</td><td></td><td>-0.001*<br>(0.001)</td></tr><tr><td>Student Dummy</td><td></td><td>-0.777<br>(1.807)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>0.853<br>(1.455)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>0.321<br>(1.272)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>-1.112<br>(1.344)</td></tr><tr><td>Risk appetite</td><td></td><td>0.381<br>(0.232)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>0.991<br>(1.110)</td></tr><tr><td>Constant</td><td>4.164<br>(2.647)</td><td>5.380<br>(3.535)</td></tr><tr><td>R<sup>2</sup></td><td>0.25</td><td>0.27</td></tr><tr><td>N</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Gap is the difference between the signal (3%) and the prior disaster prediction. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
<figure><img src="image_5.png" /><figcaption>Figure 4: Kernel densities for prior (Stage 1) and posterior (Stage 3) disaster predictions for laypersons (left panel) and experts (right panel).</figcaption></figure>

ter frequency in Stage 3 leads to a corresponding update in risky investments. In particular, the coefficient for 'Update disaster prediction' is negative and significant, indicating that an upward revision of disaster risk leads to a decrease in the share of risky investments, whose expected returns are negatively related to disaster risk.<fnref n="11" />

Overall, information treatments have a positive effect on investment levels, thus leading to portfolios that are closer to the optimal allocation. For laypersons (experts), the increase in the risky share led to an increase in expected returns by 10.5% (5.7%). In Appendix D.3, taking advantage of the data from the Control treatment in which no information was released in Stage 2, we deploy an instrumental variable strategy that confirms that the transmission of disaster expectations to risky investments is indeed causal (Table A.D.3.1).

<footnote n="11">¹¹Following Beutel et al., 2021, we add the prior risky share as a control variable in the regressions on the update in the risky share because the prior share can mechanically affect the degree to which a participant adjusts the risky share.</footnote>
<table><caption>Table 6: Linear regressions of the update of the risky share as a function of the update in disaster prediction.</caption><thead><tr><th></th><th>(1)<br>Update Risky share<br>(Stage 3)</th><th>(2)<br>Update Risky share<br>(Stage 3)</th></tr></thead><tbody><tr><td>Disaster prediction (Stage 1)</td><td>-0.137***<br>(0.045)</td><td>-0.135***<br>(0.046)</td></tr><tr><td>Update in disaster prediction (Stage 3)</td><td>-0.337***<br>(0.076)</td><td>-0.329***<br>(0.075)</td></tr><tr><td>Confidence interval (Stage 1)</td><td>0.045<br>(0.060)</td><td>0.052<br>(0.058)</td></tr><tr><td>Risky share (Stage 1)</td><td>-0.142***<br>(0.022)</td><td>-0.167***<br>(0.027)</td></tr><tr><td>Finance experts Dummy</td><td>-1.624<br>(1.515)</td><td>-0.228<br>(2.406)</td></tr><tr><td>Financial literacy</td><td>2.429***<br>(0.894)</td><td>2.044**<br>(0.896)</td></tr><tr><td>Large Sample Dummy</td><td>0.537<br>(1.496)</td><td>0.359<br>(1.493)</td></tr><tr><td>Vivid Sample Dummy</td><td>0.550<br>(1.477)</td><td>0.313<br>(1.463)</td></tr><tr><td>Age</td><td></td><td>-0.083*<br>(0.050)</td></tr><tr><td>Male Dummy</td><td></td><td>2.760*<br>(1.454)</td></tr><tr><td>Response time</td><td></td><td>0.000<br>(0.001)</td></tr><tr><td>Student Dummy</td><td></td><td>-0.916<br>(2.089)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>-3.075<br>(2.006)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>1.808<br>(1.918)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>2.617<br>(1.895)</td></tr><tr><td>Risk appetite</td><td></td><td>0.736**<br>(0.368)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>-1.904<br>(1.322)</td></tr><tr><td>Constant</td><td>1.893<br>(3.895)</td><td>0.641<br>(4.742)</td></tr><tr><td>R<sup>2</sup></td><td>0.15</td><td>0.17</td></tr><tr><td>N</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Update in disaster prediction (Stage 3) is the difference between a participant's disaster prediction after the treatment (Decision 4) and the same prediction before the treatment (Decision 1). Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Risky share (see Decision 3) is the share of the portfolio (in %) invested in the risky asset. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
Finally, regarding the impact of information on one's confidence in disaster risk predictions, we find that, in line with Hypothesis 2a, participants became more confident in their prediction following information provision as they stated narrower confidence intervals: from 17.9% to 12.7% wide for laypersons (from 13.5% to 10.6% wide for experts) (Sign Rank Tests, *p*-values < 0.001).

To assess the robustness of our results to the potential presence of outliers, we follow Fuster et al., 2022 in trimming the prior prediction at the 2.5th and 97.5th percentiles (i.e., at the 0.63% and 68% thresholds) and winsorizing the posterior prediction based on these thresholds.<fnref n="12" /> Results with outlier exclusion are presented in Table A.E.0.1 in Appendix E. They remain qualitatively identical to the results presented for the full sample, but they imply slightly higher quantitative effects of our variables of interest. Overall, this specification over a restricted sample is not our preferred specification because, unlike Fuster et al., 2022, our prior and posterior predictions are variables bounded between 0 and 100. Eliminating the 2.5th percentile results in the removal of the smallest predictions, despite the fact that they are closer to the actual value than the sample average. Furthermore, given that our preregistration documents specified that all participants would be included in the analyses, we report results for the full sample throughout the remainder of the paper.

### 4.2.2 No evidence of an effect of information precision

To test Hypothesis 2b and assess the impact of the precision of information on the magnitude of updates in disaster risk and risky investments, we use the absolute value of the update in the disaster prediction and the risky share as our key dependent variables. In Figure 5, we observe that the impact of information, as measured by the absolute value of the update in the disaster prediction and the risky share, is significant and of similar magnitude in the Small Sample and Large Sample treatments, regardless of the population. This observation is at odds with Hypothesis 2b according to which more precise information, as released in the Large Sample treatment, should have led to more substantial updates.

<footnote n="12">¹²Following Fuster et al., 2022, we winsorize rather than trim the posterior prediction, to avoid contaminating the treatment effect.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 5: Absolute update for the disaster prediction (top panels) and the risky share (bottom panels) across populations and treatments.</figcaption></figure>

Note: Average values along with 95% confidence intervals.
The results presented in Table 7 show no effect of the Large Sample treatment as the variable 'Large Sample Dummy' appears to be non-significant in explaining absolute updates in disaster prediction, absolute updates in the risky share, and updates in the confidence interval.<fnref n="13" />

These results indicate that participants do not perfectly account for the precision of information when responding to information, in contradiction to the assumptions of imperfect information models.

### 4.2.3 Laypersons react more strongly to information

Overall, the update in disaster risk following information provision was more pronounced for laypersons than for experts (-6.9% vs. -2.8%, Rank Sum Test, *p*-value < 0.001). Disaster predictions across the two populations converged in Stage 3, thus not being significantly different between laypersons and experts (14.9% vs. 12.2%, Rank Sum Test, *p*-value = 0.070).<fnref n="14" /> Furthermore, the absolute value of the update in disaster predictions, which measures the magnitude of the revision of prior predictions, was larger for laypersons than for experts (9.2% vs. 6.4%, Rank Sum Test, *p*-value < 0.001) (see also Figure 5). These findings are consistent with Hypothesis 2c.

Also in line with Hypothesis 2c, laypersons shortened their confidence interval more so than experts after information provision (-5.2% vs. -2.8%, Rank Sum Test, *p*-value < 0.001).

In line with Hypothesis 2c, laypersons increased their risky investments more so than experts after information provision although this effect was not significant (5.5% vs. 3.8%, Rank Sum Test, *p*-value = 0.095). Yet, the magnitude of the update in the risky share, which is measured as the absolute value of the update in the risky share, was significantly higher in laypersons than experts (9.9% vs. 8.7%, Rank Sum Test, *p*-value = 0.002).<fnref n="15" />

<footnote n="13">¹³We focus on absolute updates in disaster predictions and risky shares (regressions (1) to (4)) because higher information precision is expected to increase the magnitude of the updates, regardless of their sign, which depends on the sign of the gap between the signal and the prior disaster prediction. However, since information provision is expected to reduce uncertainty and thus confidence intervals in all cases, regardless of the sign of the gap, we examine the update in the confidence interval in relative terms (regressions (5) and (6)). To be able to identify any potentially different effects of the various treatments in Table 7, we no longer control for updates in the disaster risk prediction in specifications (3) to (6) because it is the variable that mediates the effect of the treatment on the risky share and on the confidence interval.</footnote>

<footnote n="14">¹⁴Given that we compare within-subject variations in predictions, the differences between experts and laypersons should not reflect differences in understanding the scoring rule in the two groups.</footnote>

<footnote n="15">¹⁵Although the difference in mean absolute updates for the risky share between the two populations is small, the difference in median updates is large, which is why the *p*-value is small.</footnote>
<table><caption>Table 7: Linear regressions assessing the impact of information precision (Large Sample treatment) on the magnitude of updates.</caption><thead><tr><th></th><th>(1)<br>Absolute<br>Update<br>Disaster<br>prediction</th><th>(2)<br>Absolute<br>Update<br>Disaster<br>prediction</th><th>(3)<br>Absolute<br>Update<br>Risky share</th><th>(4)<br>Absolute<br>Update<br>Risky share</th><th>(5)<br>Update<br>Confidence<br>interval</th><th>(6)<br>Update<br>Confidence<br>Interval</th></tr></thead><tbody><tr><td>Absolute gap</td><td>0.333***<br>(0.055)</td><td>0.333***<br>(0.054)</td><td></td><td></td><td></td><td></td></tr><tr><td>Disaster prediction (Stage 1)</td><td></td><td></td><td>-0.017<br>(0.038)</td><td>-0.024<br>(0.038)</td><td>0.060*<br>(0.035)</td><td>0.061*<br>(0.035)</td></tr><tr><td>Confidence interval (Stage 1)</td><td>0.045<br>(0.042)</td><td>0.045<br>(0.042)</td><td>0.062<br>(0.054)</td><td>0.070<br>(0.053)</td><td>-0.327***<br>(0.068)</td><td>-0.327***<br>(0.067)</td></tr><tr><td>Risky share (Stage 1)</td><td></td><td></td><td>-0.062***<br>(0.022)</td><td>-0.076***<br>(0.027)</td><td></td><td></td></tr><tr><td>Finance experts Dummy</td><td>0.235<br>(0.867)</td><td>-0.072<br>(1.133)</td><td>0.841<br>(1.379)</td><td>3.364<br>(2.303)</td><td>2.151***<br>(0.768)</td><td>2.761***<br>(1.027)</td></tr><tr><td>Financial literacy</td><td>-0.530<br>(0.500)</td><td>-0.548<br>(0.514)</td><td>-0.961<br>(0.830)</td><td>-0.824<br>(0.800)</td><td>-0.862*<br>(0.456)</td><td>-0.786<br>(0.490)</td></tr><tr><td>Large Sample Dummy</td><td>0.973<br>(0.873)</td><td>1.093<br>(0.872)</td><td>-1.125<br>(1.370)</td><td>-1.431<br>(1.350)</td><td>0.261<br>(0.727)</td><td>0.293<br>(0.715)</td></tr><tr><td>Vivid Sample Dummy</td><td>-0.209<br>(0.894)</td><td>-0.090<br>(0.901)</td><td>-1.382<br>(1.388)</td><td>-1.383<br>(1.376)</td><td>0.699<br>(0.670)</td><td>0.795<br>(0.668)</td></tr><tr><td>Age</td><td></td><td>0.003<br>(0.036)</td><td></td><td>-0.130***<br>(0.046)</td><td></td><td>0.084***<br>(0.031)</td></tr><tr><td>Male Dummy</td><td></td><td>0.092<br>(0.962)</td><td></td><td>-1.587<br>(1.363)</td><td></td><td>-1.283*<br>(0.717)</td></tr><tr><td>Response time</td><td></td><td>-0.001<br>(0.001)</td><td></td><td>0.001<br>(0.001)</td><td></td><td>-0.000<br>(0.000)</td></tr><tr><td>Student Dummy</td><td></td><td>2.017<br>(1.497)</td><td></td><td>-1.195<br>(1.887)</td><td></td><td>-0.375<br>(1.059)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>0.191<br>(1.121)</td><td></td><td>-2.088<br>(2.021)</td><td></td><td>-0.765<br>(0.972)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>0.517<br>(1.052)</td><td></td><td>2.783<br>(1.807)</td><td></td><td>-0.315<br>(0.778)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>1.681<br>(1.053)</td><td></td><td>0.062<br>(1.779)</td><td></td><td>-1.468<br>(0.900)</td></tr><tr><td>Risk appetite</td><td></td><td>0.006<br>(0.199)</td><td></td><td>0.720**<br>(0.329)</td><td></td><td>0.058<br>(0.173)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>0.092<br>(0.940)</td><td></td><td>-2.154*<br>(1.235)</td><td></td><td>0.218<br>(0.791)</td></tr><tr><td>Constant</td><td>3.650*<br>(2.100)</td><td>2.509<br>(2.950)</td><td>15.873***<br>(3.601)</td><td>16.807***<br>(4.367)</td><td>2.164<br>(2.041)</td><td>0.627<br>(2.493)</td></tr><tr><td>R<sup>2</sup></td><td>0.34</td><td>0.35</td><td>0.03</td><td>0.06</td><td>0.25</td><td>0.27</td></tr><tr><td>N</td><td>704</td><td>704</td><td>704</td><td>704</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Absolute gap is the difference in absolute terms between the signal (3%) and the prior disaster prediction. Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Risky share (see Decision 3) is the share of the portfolio (in %) invested in the risky asset. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
Although laypersons reacted more strongly to information than experts, we observe no significant differences regarding the impact of the Large Sample treatment (i.e., of the precision of information) on the magnitude of the change in the disaster prediction and the risky share across populations (see Figure 5).

Table 8 corroborates these findings as the variable 'Finance experts × Large Sample Dummy' does not reach significance regardless of the specification and the dependent variable considered. This finding is inconsistent with the second part of Hypothesis 2c. However, since this hypothesis is based on the assumption that laypersons are less informed or have less financial knowledge than experts, controlling for prior predictions, confidence intervals, and financial literacy may already account for the differences between finance experts and laypersons. Nevertheless, in Table A.F.0.1 in Appendix F, we show that these results still hold when we no longer control for absolute gap, disaster prediction, confidence interval, and financial literacy.

## 4.3 Hypothesis 3 (Behavioral models)

### 4.3.1 Updating and vividness

In Table 5, we observe that the coefficient for 'Vivid Sample Dummy' is positive yet not significant, so that the Vivid Sample treatment did not lead to significantly higher updates of disaster predictions compared to the Small Sample treatment. However, the Vivid Sample treatment led to a downward revision in disaster predictions that was less pronounced than in the Large Sample treatment (Coefficient Tests: Large Sample Dummy = Vivid Sample Dummy, *p*-values = 0.044 and 0.024 for regressions (1) and (2) in Table 5). Yet, we observe no significant differences between the Vivid and Large Sample treatments for the update in the risky share (Coefficient Tests, *p*-values = 0.986 and 0.933 for regressions (1) and (2) in Table 6). Overall, these findings are at odds with Hypothesis 3a.
<table><caption>Table 8: Linear regressions assessing the impact of being a finance expert and information precision (Large Sample treatment) on the magnitude of decision updates.</caption><thead><tr><th></th><th>(1)<br>Absolute<br>Update<br>Disaster<br>prediction</th><th>(2)<br>Absolute<br>Update<br>Disaster<br>prediction</th><th>(3)<br>Absolute<br>Update<br>Risky<br>share</th><th>(4)<br>Absolute<br>Update<br>Risky<br>share</th><th>(5)<br>Update<br>Confidence<br>interval</th><th>(6)<br>Update<br>Confidence<br>interval</th></tr></thead><tbody><tr><td>Absolute gap</td><td>0.334***<br>(0.055)</td><td>0.334***<br>(0.055)</td><td></td><td></td><td></td><td></td></tr><tr><td>Disaster prediction (Stage 1)</td><td></td><td></td><td>-0.016<br>(0.038)</td><td>-0.023<br>(0.038)</td><td>0.059*<br>(0.035)</td><td>0.060*<br>(0.035)</td></tr><tr><td>Confidence interval (Stage 1)</td><td>0.044<br>(0.042)</td><td>0.043<br>(0.042)</td><td>0.062<br>(0.054)</td><td>0.069<br>(0.052)</td><td>-0.326***<br>(0.067)</td><td>-0.326***<br>(0.067)</td></tr><tr><td>Risky share (Stage 1)</td><td></td><td></td><td>-0.062***<br>(0.022)</td><td>-0.077***<br>(0.027)</td><td></td><td></td></tr><tr><td>Finance experts Dummy</td><td>0.623<br>(1.029)</td><td>0.413<br>(1.281)</td><td>1.028<br>(1.660)</td><td>3.702<br>(2.521)</td><td>1.773**<br>(0.825)</td><td>2.316**<br>(1.115)</td></tr><tr><td>Financial literacy</td><td>-0.532<br>(0.500)</td><td>-0.552<br>(0.515)</td><td>-0.962<br>(0.831)</td><td>-0.828<br>(0.801)</td><td>-0.860*<br>(0.456)</td><td>-0.783<br>(0.490)</td></tr><tr><td>Large Sample Dummy</td><td>1.544<br>(1.164)</td><td>1.775<br>(1.154)</td><td>-0.850<br>(1.698)</td><td>-0.958<br>(1.669)</td><td>-0.297<br>(1.020)</td><td>-0.334<br>(0.993)</td></tr><tr><td>Finance experts × Large Sample Dummy</td><td>-1.196<br>(1.503)</td><td>-1.420<br>(1.491)</td><td>-0.575<br>(2.230)</td><td>-0.986<br>(2.224)</td><td>1.168<br>(1.268)</td><td>1.306<br>(1.246)</td></tr><tr><td>Vivid Sample Dummy</td><td>-0.217<br>(0.893)</td><td>-0.096<br>(0.901)</td><td>-1.386<br>(1.390)</td><td>-1.388<br>(1.377)</td><td>0.706<br>(0.671)</td><td>0.801<br>(0.668)</td></tr><tr><td>Age</td><td></td><td>0.002<br>(0.036)</td><td></td><td>-0.131***<br>(0.046)</td><td></td><td>0.085***<br>(0.031)</td></tr><tr><td>Male Dummy</td><td></td><td>0.104<br>(0.960)</td><td></td><td>-1.578<br>(1.361)</td><td></td><td>-1.294*<br>(0.716)</td></tr><tr><td>Response time</td><td></td><td>-0.001<br>(0.001)</td><td></td><td>0.001<br>(0.001)</td><td></td><td>0.000<br>(0.000)</td></tr><tr><td>Student Dummy</td><td></td><td>2.047<br>(1.497)</td><td></td><td>-1.175<br>(1.884)</td><td></td><td>-0.403<br>(1.059)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>0.165<br>(1.118)</td><td></td><td>-2.108<br>(2.024)</td><td></td><td>-0.741<br>(0.978)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>0.511<br>(1.053)</td><td></td><td>2.779<br>(1.807)</td><td></td><td>-0.310<br>(0.778)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>1.654<br>(1.052)</td><td></td><td>0.045<br>(1.785)</td><td></td><td>-1.443<br>(0.898)</td></tr><tr><td>Risk appetite</td><td></td><td>0.018<br>(0.200)</td><td></td><td>0.730**<br>(0.333)</td><td></td><td>0.048<br>(0.173)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>0.084<br>(0.942)</td><td></td><td>-2.157*<br>(1.236)</td><td></td><td>0.225<br>(0.788)</td></tr><tr><td>Constant</td><td>3.476*<br>(2.096)</td><td>2.334<br>(2.952)</td><td>15.790***<br>(3.579)</td><td>16.695***<br>(4.353)</td><td>2.338<br>(2.079)</td><td>0.791<br>(2.513)</td></tr><tr><td>R<sup>2</sup></td><td>0.34</td><td>0.35</td><td>0.03</td><td>0.06</td><td>0.25</td><td>0.27</td></tr><tr><td>N</td><td>704</td><td>704</td><td>704</td><td>704</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Absolute gap is the difference in absolute terms between the signal (3%) and the prior disaster prediction. Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Risky share (see Decision 3) is the share of the portfolio (in %) invested in the risky asset. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. Finance experts × Large Sample Dummy takes value one when a participant is a finance expert and receives the Large Sample treatment. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
4.3.2 The effect of vividness is more pronounced for laypersons

In line with Hypothesis 3b, the effect of the Vivid Sample treatment might not appear significant overall because it is confined to laypersons. Indeed, in Figure 6, we observe that the downward revision in disaster predictions is less pronounced in the Vivid Sample treatment than in the other two treatments for laypersons, whereas no such difference is observed for experts.

In Table 9, we show that, in line with Hypothesis 3b, the vividness of information limits the downward revision of prior predictions for laypersons (see coefficient for 'Vivid Sample Dummy' in regressions (1) and (2), which is positive along with *p*-values = 0.054 and 0.036). For finance experts, the effect of vividness, as measured by 'Vivid Sample Dummy + Finance experts × Vivid Sample Dummy' is not different from zero (*p*-values = 0.710 and 0.972 in regressions (1) and (2)). Similarly, when we take the Large Sample treatment as the reference treatment, we find a positive sign on the 'Vivid Sample Dummy', with *p*-values = 0.005 and 0.004, and no significant effect of vividness for finance experts. Finally, the effect of the vividness of information on the update of the risky share is not significant, regardless of the population (see regressions (3) and (4)).

Given that Hypothesis 3b hinges upon the assumption that laypersons have less accurate prior beliefs about disaster risk than finance experts and possess higher financial knowledge, controlling for prior disaster predictions and for financial literacy could mitigate the differences in the effect of the Vivid Sample treatment across populations. In Table A.F.0.2 in Appendix F we report additional results when no longer controlling for gap, prior disaster, confidence interval, and financial literacy. In this specification, the effect of receiving the Vivid Sample treatment relative to the Small Sample treatment is not significant at the 5% level for laypersons (*p*-value = 0.077), while it continues to be significant (*p*-value = 0.029) relative to the Large Sample treatment.<fnref n="16" />

<footnote n="16">¹⁶Although we did not formulate a hypothesis regarding the effect of vividness on confidence intervals, we report the results for confidence intervals in Table A.F.0.2 for the sake of completeness and find no significant effect.</footnote>
<figure><img src="image_7.png" /><figcaption>Figure 6: Prior and posterior disaster prediction for laypersons (top panels) and experts (bottom panels) across treatments.</figcaption></figure>

Note: Average values along with 95% confidence intervals.
<table><caption>Table 9: Linear regressions of the update of disaster predictions and risky shares as a function of the interaction between the type of population and the vividness of information provision.</caption><thead><tr><th></th><th>(1)<br>Update<br>Disaster prediction<br>(Stage 3)</th><th>(2)<br>Update<br>Disaster prediction<br>(Stage 3)</th><th>(3)<br>Update<br>Risky share<br>(Stage 3)</th><th>(4)<br>Update<br>Risky share<br>(Stage 3)</th></tr></thead><tbody><tr><td>Gap</td><td>0.378***<br>(0.056)</td><td>0.384***<br>(0.056)</td><td></td><td></td></tr><tr><td>Confidence interval (Stage 1)</td><td>0.087<br>(0.053)</td><td>0.084<br>(0.052)</td><td>0.015<br>(0.058)</td><td>0.024<br>(0.056)</td></tr><tr><td>Finance experts Dummy</td><td>4.588***<br>(1.194)</td><td>3.859**<br>(1.619)</td><td>-2.912*<br>(1.737)</td><td>-1.196<br>(2.554)</td></tr><tr><td>Large Sample Dummy</td><td>-1.024<br>(1.037)</td><td>-0.930<br>(1.024)</td><td>0.879<br>(1.527)</td><td>0.669<br>(1.519)</td></tr><tr><td>Vivid Sample Dummy</td><td>2.758*<br>(1.429)</td><td>2.961**<br>(1.410)</td><td>-0.080<br>(1.852)</td><td>-0.228<br>(1.840)</td></tr><tr><td>Finance experts × Vivid Sample Dummy</td><td>-3.297*<br>(1.889)</td><td>-3.012<br>(1.882)</td><td>0.503<br>(2.530)</td><td>0.136<br>(2.524)</td></tr><tr><td>Financial literacy</td><td>-1.551**<br>(0.646)</td><td>-1.512**<br>(0.671)</td><td>2.951***<br>(0.850)</td><td>2.536***<br>(0.862)</td></tr><tr><td>Age</td><td></td><td>-0.014<br>(0.044)</td><td></td><td>-0.077<br>(0.052)</td></tr><tr><td>Male Dummy</td><td></td><td>-2.879**<br>(1.159)</td><td></td><td>3.710**<br>(1.500)</td></tr><tr><td>Response time</td><td></td><td>-0.001*<br>(0.001)</td><td></td><td>0.001<br>(0.001)</td></tr><tr><td>Student Dummy</td><td></td><td>-0.747<br>(1.804)</td><td></td><td>-0.665<br>(2.192)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td></td><td>0.919<br>(1.450)</td><td></td><td>-3.364<br>(2.123)</td></tr><tr><td>Field of study (math) Dummy</td><td></td><td>0.491<br>(1.269)</td><td></td><td>1.697<br>(1.970)</td></tr><tr><td>Postgraduate education Dummy</td><td></td><td>-1.138<br>(1.345)</td><td></td><td>2.988<br>(1.991)</td></tr><tr><td>Risk appetite</td><td></td><td>0.382*<br>(0.231)</td><td></td><td>0.617*<br>(0.372)</td></tr><tr><td>Holding financial assets Dummy</td><td></td><td>0.927<br>(1.112)</td><td></td><td>-2.223<br>(1.392)</td></tr><tr><td>Disaster prediction (Stage 1)</td><td></td><td></td><td>-0.011<br>(0.041)</td><td>-0.009<br>(0.041)</td></tr><tr><td>Risky share (Stage 1)</td><td></td><td></td><td>-0.147***<br>(0.023)</td><td>-0.168***<br>(0.027)</td></tr><tr><td>Constant</td><td>3.625<br>(2.657)</td><td>4.671<br>(3.518)</td><td>0.406<br>(3.784)</td><td>-1.437<br>(4.625)</td></tr><tr><td>R<sup>2</sup></td><td>0.25</td><td>0.27</td><td>0.09</td><td>0.12</td></tr><tr><td>N</td><td>704</td><td>704</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Gap is the difference between the signal (3%) and the prior disaster prediction. Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Risky share (see Decision 3) is the share of the portfolio (in %) invested in the risky asset. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. Finance experts × Vivid Sample Dummy takes value one when a participant is a finance expert and receives the Vivid Sample treatment. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
# 5 Conclusion

Our findings provide unique evidence that, regardless of their financial expertise, economic agents hold inaccurate beliefs regarding macroeconomic disaster risk. In particular, they tend to overestimate disaster risk, leading them to underinvest in a risky asset whose return is negatively impacted by macroeconomic disasters. This finding likely reflects an availability bias according to which people give excessive weight to recent and vivid disasters such as the Covid pandemic. These results are incompatible with the standard assumption of FIRE models, and they led us to explore alternative models featuring imperfect information and behavioral biases.

According to imperfect information models, information treatments providing signals on historical disasters should help economic agents form more accurate predictions, thereby lowering their disaster risk predictions and boosting their investment levels. Our information treatments confirmed this conjecture, aligning with existing evidence that people incorporate information in a Bayesian manner when updating their beliefs about tail events (Royal, 2017; Payzan-LeNestour, 2018; Beutel et al., 2021). However, although economic agents reacted to information in the right direction, they were not fully consistent with Bayesian updating because they failed to account for the precision of the signal, reacting similarly to the Small and Large Sample treatments, regardless of whether they were laypersons or finance experts. These findings urged us to consider behavioral models, focusing on the effect of vivid information as salience is a key feature of many behavioral models of expectation formation (see e.g., Bordalo et al., 2013; Collin-Dufresne et al., 2016a; Barberis et al., 2018; Bordalo et al., 2020; Bordalo et al., 2022; Goetzmann et al., 2024).

At a practical level, our findings offer communication guidelines for financial authorities and policymakers. In particular, our results show that providing quantitative information about disaster risk can enhance the accuracy of beliefs and boost investment levels, especially for the general public. However, our results also highlight limitations in the effectiveness of information provision by showing that the precision of information is largely ignored by economic agents whether they are finance experts or laypersons. An interesting avenue for future research is to shed light on the underlying mechanisms, which are likely to differ across populations. Laypersons may not react to precise signals because of their limited cognitive skills whereas finance experts might un-
derreact due to overconfidence. Although the precision of information had no impact, we found
that the vividness of information, specifically, a one-line description of recent macroeconomic dis-
aster episodes, led laypersons to refrain from revising their disaster predictions downward. The
fact that finance experts did not react to vivid information might reflect higher cognitive skills
and extensive experience, which insulate them from the effect of vividness. In sum, our findings
show that information about disaster risk affects beliefs, and that varying the vividness of infor-
mation has more impact than varying its precision. Our results thus contribute to the still limited
empirical literature assessing the impact of communication by financial authorities, such as cen-
tral banks, on economic agents’ beliefs and decisions using survey experiments, by focusing for
the first time on extreme macroeconomic risk expectations, which play a crucial role in financial
investment decisions.

At a theoretical level, our results obtained on a sample of finance experts suggest that defending
the use of FIRE models based on the argument that critical financial decisions are made by knowl-
edgeable individuals appears unwarranted. Consistent with imperfect information models, our
findings show that information provision can play a critical role in shaping the beliefs of economic
agents, as even an imprecise signal can reduce the misperception of disaster risk among layper-
sons by half. Given the costs associated with financial literacy programs (Kaiser and Menkhoff,
2017; Kaiser et al., 2022) and their limited effectiveness (Fernandes et al., 2014), it is reassuring
that a simple information manipulation could achieve notable results. In particular, laypersons
perceived disaster risk frequency at 14.9% after receiving the information treatments, which is
comparable to the perception of experts before receiving the treatments (15.0%).

At a methodological level, our study deploys an online incentivized survey experiment that gives
us enough flexibility to collect data from finance experts while maintaining the methodological
rigor of experimental economics by providing adequate incentives and avoiding the deception of
participants. This allows us to study the impact of our information treatments on incentivized
investment decisions. We hope our approach will serve as a testbed for assessing the impact of
information provision by financial authorities on extreme macroeconomic risks, and for prevent-
ing counterproductive forms of communication, such as the release of overly optimistic Financial
Stability Reports by central banks (see Born et al., 2013).
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# Appendix

## A Translation from French to English of Instructions for finance experts

In capital letters: comments for the reader which were not part of the instructions.
Instructions were computerized.

### INSTRUCTIONS

Thank you for taking part in this online scientific study!

We are a team of researchers from CNRS, EM Lyon and Université Paris Nanterre. The study should take you no more than **twenty minutes** to complete, and is aimed at finance professionals. It focuses on the perception of the risk of macroeconomic disaster.

For taking part in this survey, you will receive a fixed gain of 15 euros once you have answered all the questions.

During the survey, you have the opportunity to earn additional money based on your choices. 1 person in 10 will be drawn at random to receive an additional gain linked to their performance in a task. Of the 6 tasks we'll ask you to complete, one will be drawn at random for the calculation of this *performance gain*. The *performance gain* will be between 0 and 60 euros for those drawn.

In addition, one participant will be drawn for an *exceptional gain* of 900 euros.

**It is very important for the quality of the study that you read and answer the questionnaire with the utmost care.** Please note that you will lose access to the questionnaire if you go back.

To continue, please first accept our questionnaire's privacy policy.

**I certify on my honor that I work in the field of finance or macroeconomics:**

- Yes

- No

**I certify on my honor that I am taking part in this study for the first time:**

- Yes

- No
## PRE-TREATMENT DECISION

We would now like to familiarize you with a few concepts that are important to our study. Please read this information carefully.

Economic researchers define **macroeconomic disasters** (MD) as extreme events with dramatic economic and financial consequences on a national scale.

A macroeconomic disaster is defined as a **cumulative** drop in GDP per capita, net of price trends, of **more than 10%** over a period of at least one year (from peak to trough) in a given country.

Macroeconomic disasters can result from economic and financial crises, wars, epidemics, natural disasters and extreme climatic events.

### Historical data file on macroeconomic disasters ("MD FILE")

Historical annual GDP per capita data - net of price changes - have been collected for 42 countries, including advanced, emerging and developing economies. The data goes back to 1870 for most countries, and at least to 1911 for others, and extends to 2021 (except for Taiwan and Venezuela, for which it extends to 2019).

These data are grouped together in a file entitled "MD FILE", including a total of 6009 data items. Each data item represents the value of GDP per capita in a country over one year.

The list of countries and periods for which data is available can be consulted by following this link (see Appendix B).

We are now going to ask you to perform 6 different tasks.

You won't have access to the MD FILE before carrying out these tasks, so you will have to make your own estimates.

At the end of the study, once you've made your decisions, you can consult the complete MD FILE by clicking on the link provided.

### Estimating the frequency of a disaster (Decision 1)

The frequency (in %) of a macroeconomic disaster in the historical database described (MD FILE) is calculated as follows:

$$ \frac{\text{Number of disasters in the MD FILE}}{\text{Total number of data in the MD FILE}} \times 100 $$

The total number of data in the MD file (= number of countries times number of years) is 6009. **The same disaster in a given country counts only once**, even if it lasts for several consecutive years. We therefore count the **number of disasters** and not the number of years of disasters in the file.
Below, we are going to ask you what you think is the frequency (in %) of a macroeconomic disaster in the MD FILE.

We realize that this question may require a little effort. For this task, your potential payoff will be higher the closer your answer is to the exact frequency of macroeconomic disasters in the MD FILE. Your gain will be calculated according to the following formula:

Gain in euros = 60 - (your estimate of frequency - exact frequency)². Obviously, your gain cannot be negative.

**Enter your estimate of the frequency** (a number between 0 and 100, representing a %, possibly non-integer).

Interval (Decision 2)

You are now asked not to estimate the exact value of the frequency of a disaster, but to choose the set of values (the interval) within which you think this frequency can lie. This indicates your degree of certainty about your previous estimate. **The wider the interval you choose, the less certain you are of the exact value you indicated in the previous task.**

Below, we will ask you to choose the lower and upper bounds of the interval within which you think the frequency of a macroeconomic disaster lies.

We realize that this question may require a little effort. For this decision, your potential gain will be calculated as follows:

- If the chosen interval does not contain the frequency of a macroeconomic disaster calculated in the MD FILE, then your gain will be zero.

- If the chosen interval contains the frequency of a macroeconomic disaster calculated in the MD FILE, then:

Gain in euros = (100 - width of chosen interval) × 0.6.

The width of the interval is the difference between its **upper** and **lower bounds**. The smaller the chosen interval, the greater your potential gain, provided the interval contains the frequency of macroeconomic disaster calculated in the MD FILE.

As a reminder, in the previous question, **the exact value you gave for the frequency of a macroeconomic disaster was ...%**.

**Now choose below the lower and upper bounds of the interval in which you think the frequency of a macroeconomic disaster lies** (choose two numbers between 0 and 100, representing %, possibly non-integer; the lower bound must be smaller than or equal to the upper bound):

Lower bound:

Upper bound:
Investment decision (Decision 3)

Let's now consider the following scenario: you need to invest €100,000 on your own account.
You must allocate this sum over a 1-year period between:

- A **risk-free asset (A)** with a guaranteed return of 1%;

- And a **portfolio of risky assets (B)** with a variable return.

The return on risky assets (B) depends on whether or not a macroeconomic disaster has occurred. We will randomly select a data item from the MD FILE that corresponds either to the absence of a disaster, or to the occurrence of a disaster. NB: A disaster is counted only once in the file, regardless of its duration.

- If the data drawn corresponds to the absence of a disaster, the return on risky assets (B) will be +10%.

**The final value of your portfolio** will be equal to: 100,000 + 1% of the amount you invested in risk-free assets (A) + 10% of the amount you invested in risky assets (B).

- If the data drawn corresponds to the occurrence of a disaster, the return on risky assets (B) will be -30%.

**The final value of your portfolio** will be equal to: 100,000 + 1% of the amount you invested in risk-free assets (A) - 30% of the amount you invested in risky assets (B).

In both cases, your potential gain will be calculated as follows: Gain in euros = final value of your portfolio/2000.

Please indicate below the amount you wish to invest in the risk-free asset (A) and the amount you wish to invest in the risky asset portfolio (B):

- Risk-free asset (A)

- Risky asset (B)

We are now going to give you some information. Please read it carefully. You can click on the "Next" button once you've spent at least 15 seconds on this page.

TREATMENT 1: LOW INFORMATION PRECISION

Of the 6,009 data items in the MD file, 100 were analyzed in detail. These 100 data were drawn from the MD FILE without focusing on a given country or year, to guarantee the statistical validity of the sample. Among these 100 data, 3 different macroeconomic disasters were observed.
## TREATMENT 2: HIGH INFORMATION PRECISION

Of the 6,009 data items in the MD file, 1,000 were analyzed in detail. These 1,000 data were drawn from the MD FILE without focusing on a given country or year, to guarantee the statistical validity of the sample. Among these 1,000 data, 30 different macroeconomic disasters were observed.

## TREATMENT 3: INFORMATION SALIENCE

Of the 6,009 data items in the MD file, 100 were analyzed in detail. These 100 data were drawn from the MD FILE without focusing on a given country or year, to guarantee the statistical validity of the sample. Among these 100 data, 3 different macroeconomic disasters were observed. Among these 100 data, 3 different macroeconomic disasters were observed. The first corresponds to the 2008-2013 period in Greece (consequence of the financial crisis), the second corresponds to 2020 in the UK (consequence of the Covid pandemics) and the third corresponds to 2020 in Spain (also consequences of the Covid pandemics).

Reminder: A macroeconomic disaster (MD) is defined as a cumulative drop in GDP per capita, net of price trends, of more than 10% over a period of at least one year (from peak to trough) in a given country.

What do you think the inflation rate in France will be over the next 12 months? (in %)

NB: The inflation rate is the growth rate of the general level of consumer prices over a given period. It can be positive or negative.
What do you think will be the approximate growth rate of economic activity in France over the next 12 months? (in %)

NB: The growth rate of economic activity is the growth rate of an economy's output (measured by GDP) over a given period. It can be positive or negative.

## POST-TREATMENT DECISIONS

We are now going to ask you to carry out the three initial tasks again: estimate the frequency of a macroeconomic disaster, estimate the interval within which this frequency may lie, and make an investment decision.

### Estimating the frequency of a disaster (Decision 4)

The frequency (in %) of a macroeconomic disaster in the historical database described (MD FILE) is calculated as follows:

$$ \frac{\text{Number of disasters in the MD FILE}}{\text{Total number of data in the MD FILE}} \times 100 $$

The total number of data in the MD file (= number of countries times number of years) is 6,009. **The same disaster in a given country counts only once**, even if it lasts for several consecutive years. We therefore count the **number of disasters** and not the number of years of disasters in the file.

Below, we are going to ask you what you think is the **frequency (in %)** of a macroeconomic disaster in the MD FILE.

We realize that this question may require a little effort. For this task, your potential payoff will be higher the closer your answer is to the exact frequency of macroeconomic disasters in the MD FILE. Your gain will be calculated according to the following formula:

Gain in euros = 60 - (your estimate of frequency - exact frequency)². Obviously, your gain cannot be negative.

**Enter your estimate of the frequency** (a number between 0 and 100, representing a %, possibly non-integer).

### Interval (Decision 5)

You are now asked not to estimate the exact value of the frequency of a disaster, but to choose the set of values (the interval) within which you think this frequency can lie. This indicates your degree of certainty about your previous estimate. **The wider the interval you choose, the less certain you are of the exact value you indicated in the previous task.**
Below, we will ask you to choose the lower and upper bounds of the interval within which you think the frequency of a macroeconomic disaster lies.

We realize that this question may require a little effort. For this decision, your potential gain will be calculated as follows:

- If the chosen interval does not contain the frequency of a macroeconomic disaster calculated in the MD FILE, then your gain will be zero.

- If the chosen interval contains the frequency of a macroeconomic disaster calculated in the MD FILE, then:

Gain in euros = (100 - width of chosen interval) × 0.6.

The width of the interval is the difference between its upper and lower bounds. The smaller the chosen interval, the greater your potential gain, provided the interval contains the frequency of macroeconomic disaster calculated in the MD FILE.

As a reminder, in the previous question, **the exact value you gave for the frequency of a macroeconomic disaster was ...%**.

**Now choose below the lower and upper bounds of the interval in which you think the frequency of a macroeconomic disaster lies** (choose two numbers between 0 and 100, representing %, possibly non-integer; the lower bound must be smaller than or equal to the upper bound):

Lower bound:

Upper bound:

### Investment decision (Decision 6)

Let's now consider the following scenario: you need to invest €100,000 on your own account. You must allocate this sum over a 1-year period between:

- A **risk-free asset (A)** with a guaranteed return of 1%;

- And a **portfolio of risky assets (B)** with a variable return.

The return on risky assets (B) depends on whether or not a macroeconomic disaster has occurred. We will randomly select a data item from the MD FILE that corresponds either to the absence of a disaster, or to the occurrence of a disaster. NB: A disaster is counted only once in the file, regardless of its duration.
- If the data drawn corresponds to the absence of a disaster, the return on risky assets (B) will be +10%.

**The final value of your portfolio** will be equal to: 100,000 + 1% of the amount you invested in risk-free assets (A) + 10% of the amount you invested in risky assets (B).

- If the data drawn corresponds to the occurrence of a disaster, the return on risky assets (B) will be -30%.

**The final value of your portfolio** will be equal to: 100,000 + 1% of the amount you invested in risk-free assets (A) - 30% of the amount you invested in risky assets (B).

In both cases, your potential gain will be calculated as follows: Gain in euros = final value of your portfolio/2000.

Please indicate below the amount you wish to invest in the risk-free asset (A) and the amount you wish to invest in the risky asset portfolio (B):

* Risk-free asset (A)

* Risky asset (B)

## POST-EXPERIMENTAL QUESTIONNAIRE

We now ask you to answer some brief questions about your knowledge of economics and your socio-demographic characteristics. This should take less than 5 minutes.

In general, how willing are you to take risks? Select a number between 0 and 10. A value of 0 means "not at all willing to take risks" and a value of 10 means "very willing to take risks".

Not at
all 1 2 3 4 5 6 7 8 9 Very willing 10
0

Do you own a portfolio of financial assets?

- Yes

- No

Suppose you have €100 in a savings account and the annual interest rate is 2%. After five years, how much do you think you'll have if you let this money grow?

- More than €102

- Exactly €102

- Less than €102

- Don't know

- Don't want to answer
Suppose the annual interest rate on your savings account is 1% and the annual inflation rate is 2%. After one year, your savings will enable you to buy...

- More than today

- Exactly as much as today

- Less than today

- Don't know

- Don't want to answer

When interest rates rise, what usually happens to bond prices?

- They rise

- They fall

- They stay the same

- There is no relationship between interest rates and bond prices

- Don't know

- Don't want to answer

A 15-year mortgage generally requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be lower:

- True

- False

- Don't know

- Don't want to answer

Buying shares in a single company provides a more secure return than an equity mutual fund:

- True

- False

- Don't know

- Don't want to answer

Are you?:

- Female

- Male

- Other
What is your age?

What is the highest diploma you have obtained?

- No diploma, brevet des collèges, CAP, BEP or equivalent

- Baccalaureate

- Bac +2

- Bac +3

- Bac +4

- Bac +5

- Higher than Bac +5

What was your main field of study?

- Economics

- Management

- Finance

- Actuarial science, statistics

- Humanities

- Law

- Mathematics, Computer Science, Physics-Chemistry

- Biology, Life and Earth Sciences

- Medicine

- Other<fnref n="12" />

<footnote n="12">¹²Laypersons were provided with an additional option: “I didn’t have higher education”.</footnote>
What is your current field of employment (multiple answers possible):

- Proprietary trading

- Trading for third parties

- Sale of financial products

- Market making

- Portfolio/asset management

- Strategic analysis/economic analysis/economic monitoring

- Financial analysis

- ESG analysis

- Quantitative analysis

- Risk analysis

- Private equity management

- Marketing

- Other

What type of market do you specialize in (multiple answers possible)? If you are not directly involved, please tick "Other".

- Money market

- Foreign exchange

- Equities

- Bonds (fixed income)

- Commodities

- Private equity

- Hedge funds

- Real estate

- Other
Who is your current employer?

- A bank

- An asset management company

- A wealth management consultancy

- An online broker

- A broker

- A robo-advisor

- A hedge fund

- A private equity firm

- An institutional investor managing for its own account

- A public institution

- Yourself

- Other

## FOR THE PARTICIPANTS WHO WERE RANDOMLY SELECTED TO GET THE PERFORMANCE EARNING

### GAIN

You are one of the participants who have been drawn at random to receive a prize linked to your performance in one of the 6 tasks (drawn at random) you have completed. The task drawn for you is Decision ....

Your payout for your performance on this task is ...€.

Since the participation gain is €15, your total gain is ... €.

The study is now complete. Thank you for participating!

To proceed to payment, click here.

You can view the full MD file. Disaster periods are shown in red.

If you have any questions or comments, please contact us at: desastremacro@gmail.com

To receive further invitations to participate in scientific studies in economics, you can register here.

## FOR THE PARTICIPANTS WHO WERE NOT RANDOMLY SELECTED TO GET THE PERFORMANCE EARNING

### GAIN

The study is now complete. Thank you for participating!

Your participation gain is €15.
You are not among the participants drawn to receive an additional prize linked to your performance on the tasks performed, but you may be drawn for the 900 euro lottery when the study is completed.

To proceed to payment, click here.

You can view the full MD file. Disaster periods are shown in red.

If you have any questions or comments, please contact us at: desastremacro@gmail.com

To receive further invitations to participate in scientific studies in economics, you can register here.

# INSTRUCTIONS SPECIFIC TO THE SURVEY FOR LAYPERSONS

## NUMERICAL LITERACY TEST

In a TV show, the probability to win a car is 1 out of 1,000. Which percentage of the participants to the TV show wins a car? Answer below in %. If you need to write decimals, you can use ".".

In the jungle, 50% of frogs are red and 50% are yellow. The probability that a red frog is toxic is 20% and the probability that a yellow frog is toxic is 5%. What is the probability that a toxic frog is red (in %)?

## ADDITIONAL SOCIO-DEMOGRAPHIC QUESTION

What is your occupational category?<fnref n="13" />

- Farmer

- Artisan, retailer, self-employed

- Executive or intellectual profession

- Middle-level profession

- Employee

- Worker

- Retired

- Student

- Unemployed

<footnote n="13">¹³ These occupational categories are based on the INSEE (French institute of statistics and economic studies) nomenclature.</footnote>
B List of countries and years considered in the dataset (the 'MD' file)

<table><caption>Table A.B.0.1: Sample included in the 'MD' file (updated Barro and Ursua, 2008 dataset).</caption><thead><tr><th>Country</th><th>Years</th><th>Country</th><th>Years</th><th>Country</th><th>Years</th></tr></thead><tbody><tr><td>Argentina</td><td>1875-2021</td><td>Greece</td><td>1870-2021</td><td>Russia</td><td>1870-2021</td></tr><tr><td>Australia</td><td>1870-2021</td><td>Iceland</td><td>1870-2021</td><td>Singapore</td><td>1900-2021</td></tr><tr><td>Austria</td><td>1870-2021</td><td>India</td><td>1872-2021</td><td>South Africa</td><td>1911-2021</td></tr><tr><td>Belgium</td><td>1870-2021</td><td>Indonesia</td><td>1880-2021</td><td>South Korea</td><td>1911-2021</td></tr><tr><td>Brazil</td><td>1870-2021</td><td>Italy</td><td>1870-2021</td><td>Spain</td><td>1870-2021</td></tr><tr><td>Canada</td><td>1870-2021</td><td>Japan</td><td>1870-2021</td><td>Sri Lanka</td><td>1870-2021</td></tr><tr><td>Chile</td><td>1870-2021</td><td>Malaysia</td><td>1900-2021</td><td>Sweden</td><td>1870-2021</td></tr><tr><td>China</td><td>1890-2021</td><td>Mexico</td><td>1895-2021</td><td>Switzerland</td><td>1870-2021</td></tr><tr><td>Colombia</td><td>1905-2021</td><td>Netherlands</td><td>1870-2021</td><td>Taiwan</td><td>1901-2019</td></tr><tr><td>Denmark</td><td>1870-2021</td><td>New-Zealand</td><td>1870-2021</td><td>Turkey</td><td>1875-2021</td></tr><tr><td>Egypt</td><td>1894-2021</td><td>Norway</td><td>1870-2021</td><td>United Kingdom</td><td>1870-2021</td></tr><tr><td>Finland</td><td>1870-2021</td><td>Peru</td><td>1896-2021</td><td>United States</td><td>1870-2021</td></tr><tr><td>France</td><td>1870-2021</td><td>Philippines</td><td>1902-2021</td><td>Uruguay</td><td>1870-2021</td></tr><tr><td>Germany</td><td>1870-2021</td><td>Portugal</td><td>1870-2021</td><td>Venezuela</td><td>1883-2019</td></tr></tbody></table>
# C Balance tables and descriptive statistics

## C.1 Balance tables for the main experiment (2023)

<table><caption>Table A.C.1.1: Balance tables per treatment and population.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Finance experts</th><th rowspan="2">p-value<br>(multiv. t-test for ≠ across treatm.)</th><th colspan="3">Laypersons</th><th rowspan="2">p-value<br>(multiv. t-test for ≠ across treatm.)</th><th rowspan="2">p-value<br>(t-test for ≠ between experts &amp; layp.)</th></tr><tr><th>Small Sample</th><th>Large Sample</th><th>Vivid Sample</th><th>Small Sample</th><th>Large Sample</th><th>Vivid Sample</th></tr></thead><tbody><tr><td>N</td><td>116</td><td>106</td><td>124</td><td></td><td>120</td><td>121</td><td>117</td><td></td><td></td></tr><tr><td>Female</td><td>31%</td><td>27%</td><td>19%</td><td>0.11</td><td>48%</td><td>53%</td><td>45%</td><td>0.50</td><td>&lt;0.01</td></tr><tr><td>Age</td><td>35</td><td>34</td><td>36</td><td>0.61</td><td>30</td><td>30</td><td>29</td><td>0.37</td><td>&lt;0.01</td></tr><tr><td>Education ≥ Bac +4</td><td>98%</td><td>95%</td><td>94%</td><td>0.28</td><td>52%</td><td>57%</td><td>54%</td><td>0.70</td><td>&lt;0.01</td></tr><tr><td>Financial literacy score</td><td>4.6/5</td><td>4.6/5</td><td>4.7/5</td><td>0.67</td><td>3.5/5</td><td>3.6/5</td><td>3.6/5</td><td>0.78</td><td>&lt;0.01</td></tr><tr><td>Max. financial literacy score</td><td>71%</td><td>75%</td><td>76%</td><td></td><td>1.4/2</td><td>1.3/2</td><td>1.3/2</td><td>0.90</td><td></td></tr><tr><td>Numerical literacy score</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Holding financial assets</td><td>81%</td><td>76%</td><td>77%</td><td>0.63</td><td>43%</td><td>40%</td><td>44%</td><td>0.82</td><td>&lt;0.01</td></tr><tr><td>Inflation forecast</td><td>4.88%</td><td>5.35%</td><td>5.85%</td><td>0.22</td><td>6.81%</td><td>6.02%</td><td>6.95%</td><td>0.67</td><td>0.02</td></tr><tr><td>Growth forecast</td><td>2.21%</td><td>2.93%</td><td>2.73%</td><td>0.45</td><td>2.74%</td><td>3.41%</td><td>4.28%</td><td>0.35</td><td>0.09</td></tr><tr><td>Student</td><td></td><td></td><td></td><td></td><td>29%</td><td>22%</td><td>29%</td><td>0.48</td><td></td></tr><tr><td>Field of study: eco./finance</td><td>83%</td><td>80%</td><td>80%</td><td>0.83</td><td>6%</td><td>7%</td><td>6%</td><td>0.86</td><td>&lt;0.01</td></tr><tr><td>Field of study: math.</td><td>4%</td><td>9%</td><td>10%</td><td>0.23</td><td>32%</td><td>26%</td><td>21%</td><td>0.15</td><td>&lt;0.01</td></tr><tr><td>Risk appetite</td><td>6.4/10</td><td>6.7/10</td><td>6.5/10</td><td>0.30</td><td>5.2/10</td><td>4.8/10</td><td>4.9/10</td><td>0.35</td><td>&lt;0.01</td></tr><tr><td>Sector: economic/risk analysis</td><td>26%</td><td>27%</td><td>25%</td><td>0.92</td><td>—</td><td>—</td><td>—</td><td></td><td></td></tr><tr><td>Sector: trading/fin. markets</td><td>9%</td><td>10%</td><td>10%</td><td>0.90</td><td>—</td><td>—</td><td>—</td><td></td><td></td></tr><tr><td>Sector: asset/wealth man./sales</td><td>24%</td><td>30%</td><td>38%</td><td>0.07</td><td>—</td><td>—</td><td>—</td><td></td><td></td></tr><tr><td>Sector: financial analysis</td><td>28%</td><td>29%</td><td>24%</td><td>0.57</td><td>—</td><td>—</td><td>—</td><td></td><td></td></tr><tr><td>Function related to stock market</td><td>52%</td><td>42%</td><td>46%</td><td>0.31</td><td>—</td><td>—</td><td>—</td><td></td><td></td></tr><tr><td>Response time in min</td><td>12</td><td>11</td><td>13</td><td>0.28</td><td>11</td><td>14</td><td>12</td><td>0.08</td><td></td></tr><tr><td>Total completion time in min</td><td>16</td><td>16</td><td>18</td><td>0.31</td><td>18</td><td>21</td><td>19</td><td>0.06</td><td></td></tr></tbody></table>

Notes: Variables represent means or shares of variables in each treatment and group. The field of study 'math.' includes mathematics, computer science, physics-chemistry, actuarial science and statistics. The post-experimental survey is different for experts and laypersons, which can generate differences in total completion time. Therefore, we also provide the response time, before the post-experimental survey. We use multivariate t-tests to test the significance of differences between the three treatment arms and standard t-tests to test the significance of differences between experts and laypersons.

Relying on multivariate t-tests for mean comparisons between treatments for finance experts and laypersons respectively, we find evidence validating our randomization, as the means of the key individual characteristics do not differ significantly between treatments. Based on t-tests for mean comparisons between our sample of finance experts and our sample of laypersons, we find that, as expected, the two samples differ significantly in terms of gender distribution, age, financial literacy, education, risk appetite, holding of financial assets, and field of study.
## C.2 Socio-demographic characteristics of our sample relative to the general French population

<table><caption>Table A.C.2.1: Comparison between our two samples and the French population.</caption><thead><tr><th></th><th>Finance<br>Experts<br>Sample</th><th>Laypersons<br>Sample</th><th>French<br>Population</th><th>p-value<br>paired<br>t-test<br>Experts<br>vs. French<br>Population</th><th>p-value<br>paired<br>t-test<br>Laypersons<br>vs. French<br>Population</th></tr></thead><tbody><tr><td>Proportion of women (%)</td><td>26</td><td>49</td><td>51</td><td>&lt;0.001</td><td>0.42</td></tr><tr><td>Average age</td><td>35</td><td>30</td><td>42</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>Financial literacy (average)</td><td>4.62/5</td><td>3.59/5</td><td>2.43/5</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>Education level %</td><td></td><td></td><td>(for 25-34 years old)</td><td></td><td></td></tr><tr><td>High school graduates</td><td>100</td><td>98</td><td>75</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>Associate degree ('bac+2')</td><td>1</td><td>11</td><td>12</td><td>&lt;0.001</td><td>0.50</td></tr><tr><td>Bachelor's or Master's ('bac+3' or 'bac+4')</td><td>8</td><td>28</td><td>14</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>Postgraduate degree ('bac+5' or higher)</td><td>90</td><td>47</td><td>25</td><td>&lt;0.001</td><td>&lt;0.001</td></tr></tbody></table>

*Notes:* Data on the proportion of women, average age in the French population and education level in 2023 come from the INSEE (see in particular the INSEE Tableau de bord de l'économie française, the INSEE Census data and the INSEE Portrait Social de la France. As education level in the French population is provided by age groups and gender, we focus on data for the 25-34 years old in line with the average age in our samples of study and we average the data based on the gender proportions in the population. The data on the financial literacy score is the average financial knowledge score from the Eurobarometer survey conducted in France in 2023 which includes 5 questions, among which 4 questions are identical to those in our survey. We provide the *p*-values of paired t-tests to test whether our sample of finance experts and our sample of laypersons differ significantly from the general French population based on some key socio-demographic characteristics.

As expected, the sample of finance experts differs significantly from the general French population in what regards all key sociodemographic characteristics (with the *p*-values of all paired t-tests < 0.001). Our pool of finance experts includes more men, is younger, more educated, and has a higher average financial literacy score than the general French population. However, our sample of laypersons also shows statistically significant differences from the general population: our sample does not differ in terms of gender proportion, but our respondents are younger and more educated than the general French population, and have a higher level of financial literacy. This finding suggests that our study likely provides a lower bound for the differences between finance experts and laypersons.
## C.3 Descriptive statistics pre and post-treatment

<table><caption>Table A.C.3.1: Descriptive statistics pre- and post-treatment.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Experts</th><th rowspan="2">p-value<br>(multiv.<br>t-test<br>for ≠<br>between<br>treatm.)</th><th colspan="3">Laypersons</th><th rowspan="2">p-value<br>(multiv.<br>t-test<br>for ≠<br>between<br>treatm.)</th></tr><tr><th>Small<br>Sample</th><th>Large<br>Sample</th><th>Vivid<br>Sample</th><th>Small<br>Sample</th><th>Large<br>Sample</th><th>Vivid<br>Sample</th></tr></thead><tbody><tr><td><strong>Prior</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Disaster frequency (%)</td><td>15.5</td><td>14.9</td><td>14.6</td><td>0.91</td><td>22.5</td><td>20.2</td><td>22.6</td><td>0.58</td></tr><tr><td>Interval width (uncertainty)</td><td>14.2</td><td>13.6</td><td>13.0</td><td>0.80</td><td>16.4</td><td>19.1</td><td>18.3</td><td>0.35</td></tr><tr><td>Share in the risky asset (%)</td><td>55.8</td><td>52.0</td><td>51.7</td><td>0.53</td><td>39.4</td><td>39.3</td><td>39.8</td><td>0.99</td></tr><tr><td><strong>Posterior</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Disaster frequency (%)</td><td>12.6</td><td>12.2</td><td>11.8</td><td>0.93</td><td>15.2</td><td>11.8</td><td>17.6</td><td>0.05</td></tr><tr><td>Interval width (uncertainty)</td><td>11.2</td><td>10.7</td><td>10.0</td><td>0.75</td><td>11.1</td><td>13.0</td><td>14.1</td><td>0.23</td></tr><tr><td>Share in the risky asset (%)</td><td>57.9</td><td>57.3</td><td>55.8</td><td>0.87</td><td>45.1</td><td>44.9</td><td>44.9</td><td>1.00</td></tr></tbody></table>

*Notes*: Means of each variable are displayed pre-treatment ('Prior') and post-treatment ('Posterior') and per population. We use multivariate t-tests to test the significance of differences between the three treatment arms.

T-tests for mean comparisons show the integrity of our randomization for our main variables of interest, as our prior predictions and decisions do not differ significantly between treatment arms, for both finance experts and laypersons (p-values > 0.01).

## D Robustness checks using the Control treatment for between-subject comparisons

We conducted an additional test on laypersons on Prolific in June 2024 to provide several robustness checks. We added a control group in which participants received no additional information between Stage 1 and Stage 3. Another group of participants received the Small Sample treatment. Table A.D.0.1 presents the balance tables for this additional experiment.
<table><caption>Table A.D.0.1: Balance tables between Control and Small Sample treatment (June 2024 additional experiment)</caption><thead><tr><th>Variable</th><th>Control</th><th>Small Sample</th><th>p-value for t-test</th></tr></thead><tbody><tr><td>N</td><td>117</td><td>115</td><td>—</td></tr><tr><td>Female</td><td>45%</td><td>44%</td><td>0.69</td></tr><tr><td>Age</td><td>31</td><td>30</td><td>0.78</td></tr><tr><td>Education ≥ Bac +4</td><td>54%</td><td>61%</td><td>0.28</td></tr><tr><td>Financial literacy score</td><td>3.7/5</td><td>3.9/5</td><td>0.07</td></tr><tr><td>Max. financial literacy score</td><td>26%</td><td>34%</td><td>0.08</td></tr><tr><td>Numerical literacy score</td><td>1.4/2</td><td>1.6/2</td><td>0.03</td></tr><tr><td>Holding financial assets</td><td>42%</td><td>36%</td><td>0.58</td></tr><tr><td>Inflation forecast</td><td>4.1%</td><td>5.3%</td><td>0.23</td></tr><tr><td>Growth forecast</td><td>2.0%</td><td>2.7%</td><td>0.19</td></tr><tr><td>Student</td><td>23%</td><td>27%</td><td>0.28</td></tr><tr><td>Field of study: eco./finance</td><td>4%</td><td>9%</td><td>0.17</td></tr><tr><td>Field of study: math.</td><td>26%</td><td>25%</td><td>0.82</td></tr><tr><td>Risk appetite</td><td>4.6/10</td><td>4.8/10</td><td>0.48</td></tr><tr><td>Response time in min</td><td>11</td><td>12</td><td>0.01</td></tr><tr><td>Total completion time in min</td><td>17</td><td>20</td><td>0.03</td></tr></tbody></table>

*Notes*: Variables represent means or shares of variables in each treatment and group. The field of study 'math.' includes mathematics, computer science, physics-chemistry, actuarial science and statistics. The post-experimental survey is different for experts and laypersons, which can generate differences in total completion time. Therefore, we also provide the response time, before the post-experimental survey.

We can draw three results from this additional experiment.

## D.1 Robustness over time

First, we find no significant differences between prior macroeconomic disaster predictions of laypersons in November 2023 and June 2024 (21.8% vs. 23.0%, Rank Sum test, *p*-value=0.585) nor between confidence intervals (17.9% vs. 16.8%, Rank Sum test, *p*-value=0.582) or prior risky shares (39.5% vs. 37.7%, Rank Sum test, *p*-value=0.426). These findings show that our results are robust over time, and that any potential time variation in beliefs occurs slowly.
## D.2 Alleviating concerns of numerical anchoring

Second, the additional experiment enables us to alleviate potential concerns of numerical anchoring (Furnham and Boo, 2011) that could arise due to the quantitative nature of the information provided in the treatment (Haaland et al., 2023). Indeed, we can compare participants’ forecasts for inflation and growth in the next 12 months in France in the control group and treated group of the experiment conducted in June 2024.<fnref n="17" /> Hence, these forecasts were asked between Stage 1 and Stage 3, implying that they were provided just after the treatment and before the elicitation of posterior predictions in the treated group. Given that the information provided in the treatment is not directly related to the forecasts of inflation and growth in the next 12 months in France, we should find no significant differences in these forecasts in the control and the treated groups, unless numerical anchoring takes place in the treated group.

We find that forecasts do not significantly differ in the control and treated groups (with a mean of 4.1% in the former vs. 5.3% in the latter, Rank Sum test, *p*-value=0.300 for inflation and a mean of 2.0% in the control group vs. 2.7% in the treated group, Rank Sum test, *p*-value=0.234 for growth). This result ensures that participants in the treated groups update their posterior prediction of disaster risk in the direction of the gap between the signal provided and their prior prediction because they consider that the treatment provides new and relevant information about this prediction, and not because they stick to the numerical value shown in the quantitative treatment, whatever the question.

## D.3 Causal impact of beliefs on decisions

Third, the introduction of a control group in the additional experiment allows us to conduct an instrumental variable analysis to mitigate potential concerns about endogeneity between beliefs and decisions, and to properly identify the causal impact of the former on the latter. With this aim in view, we pool together all the participants who were provided with additional information about the past frequency of macroeconomic disasters in both the 2023 and 2024 experiments, and compare their updating behavior to that of participants in the control group of the 2024 experi-

<footnote n="17">¹⁷Given that inflation and growth forecasts for the next 12 months are changing over time, we only compare forecasts of the control and treated groups in the experiment conducted in June 2024.</footnote>
ment. Thus, we construct an Information Dummy which takes value one if a participant received any information treatment in the 2023 or 2024 experiment, whatever the information treatment they received (either the Small Sample treatment, the Large Sample treatment, or the Vivid Treatment), and 0 if a participant was allocated to the control group in the 2024 experiment. Then, we instrument the revision in the disaster prediction between Stage 1 and Stage 3 with the exogenous variation generated by receiving additional information about the past frequency of macroeconomic disasters relative to being in the control group. Finally, we use the exogenous variation in the disaster prediction revision obtained in this first step to causally explain the variation in the share invested in the risky asset. More specifically, we estimate the following set of equations by 2SLS:

$$ \text{Update Risky share}_i = \beta_0 + \beta_1 \widehat{\text{Update Disaster Prediction}}_i + \beta_2 \text{Disaster Prediction}_i \text{ (Stage 1)} + \\ \beta_3 \text{Risky share}_i \text{ (Stage 1)} + \Omega^T \text{Control}_i + u_i, $$

where $\widehat{\text{Update Disaster Prediction}}_i$ is estimated as follows:

$$ \widehat{\text{Update Disaster Prediction}}_i = \widehat{\alpha}_0 + \widehat{\alpha}_1 \text{Information Dummy}_i + \widehat{\alpha}_2 \text{Disaster Prediction}_i \text{ (Stage 1)} + \\ \widehat{\alpha}_3 \text{Risky share}_i \text{ (Stage 1)} + \widehat{\Theta}^T \text{Control}_i, $$

where $\widehat{\text{Control}}_i$ represents the vector of socio-demographic controls which are included to increase the precision of the estimation of the treatment effect.<fnref n="18" />

The results of the IV estimation presented in Table A.D.3.1 confirm that the negative and significant impact of the revision in the disaster prediction on the revision in the risky share is causal. Over the full sample of participants, we find that a 1% decrease in the disaster prediction leads to a 0.6% increase in the portfolio share invested in the risky asset.

<footnote n="18">¹⁸We also control for the prior disaster prediction and risky share (in Stage 1) to account for potential mechanical correlations that could arise between prior predictions and revisions, following Beutel et al., 2021. However, our IV estimate is similar in significance and magnitude that we control for the prior disaster prediction or not (and similarly, that we control for prior confidence interval and prior risky share or not).</footnote>
<table><caption>Table A.D.3.1: Causal effect of the update in the disaster prediction on the update of the risky share: IV estimation.</caption><thead><tr><th></th><th>(1)<br>2SLS<br>Update Risky share<br>(Stage 3)</th></tr></thead><tbody><tr><td>Update in disaster prediction (Stage 3)</td><td>-0.629***<br>(0.232)</td></tr><tr><td>Disaster prediction (Stage 1)</td><td>-0.158*<br>(0.091)</td></tr><tr><td>Risky share (Stage 1)</td><td>-0.166***<br>(0.024)</td></tr><tr><td>Finance experts Dummy</td><td>-0.252<br>(2.030)</td></tr><tr><td>Financial literacy</td><td>1.555*<br>(0.828)</td></tr><tr><td>Age</td><td>-0.047<br>(0.047)</td></tr><tr><td>Male Dummy</td><td>2.306*<br>(1.335)</td></tr><tr><td>Response time</td><td>-0.000<br>(0.001)</td></tr><tr><td>Student Dummy</td><td>-0.723<br>(1.732)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td>-1.733<br>(1.806)</td></tr><tr><td>Field of study (math) Dummy</td><td>1.621<br>(1.475)</td></tr><tr><td>Postgraduate education Dummy</td><td>2.265<br>(1.416)</td></tr><tr><td>Risk appetite</td><td>1.042***<br>(0.353)</td></tr><tr><td>Holding financial assets Dummy</td><td>-1.050<br>(1.157)</td></tr><tr><td>Constant</td><td>-0.456<br>(4.263)</td></tr><tr><td>R<sup>2</sup></td><td>0.13</td></tr><tr><td>N</td><td>936</td></tr></tbody></table>

Notes: Update in disaster prediction (Stage 3) is the predicted value of the difference between a participant's disaster prediction after the treatment (Decision 4) and the same prediction before the treatment (Decision 1) based on a first-step estimation with the Information Dummy (which takes value one when a participant received any of the information treatments and 0 when a participant was in the control group) and the following variables as dependent variables. Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Risky share (see Decision 3) is the share of the portfolio (in %) invested in the risky asset. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
E Robustness checks with outlier exclusion

To account for the potential role of outliers in our sample, we follow Fuster et al., 2022 in trimming
the prior prediction at the 2.5th and 97.5th percentiles (i.e., at 0.63% and 68%) and winsorizing
the posterior prediction based on the 0.63% and 68% thresholds. This implies dropping 34 obser-
vations. We winsorize rather than trim the posterior prediction, because dropping some of our
observations based on post-treatment results could contaminate the treatment effect. Table A.E.0.1
shows that our results are robust to outlier exclusion.
<table><caption>Table A.E.0.1: Linear regressions of the main estimations with outlier exclusion.</caption><thead><tr><th></th><th>(1)<br>Update<br>Disaster prediction<br>(Stage 3)</th><th>(2)<br>Update<br>Risky share<br>(Stage 3)</th></tr></thead><tbody><tr><td>Gap</td><td>0.416<sup>***</sup><br>(0.046)</td><td></td></tr><tr><td>Disaster prediction (Stage 1)</td><td></td><td>-0.130<sup>**</sup><br>(0.065)</td></tr><tr><td>Update in disaster prediction (Stage 3)</td><td></td><td>-0.423<sup>***</sup><br>(0.092)</td></tr><tr><td>Confidence interval (Stage 1)</td><td>0.096<sup>*</sup><br>(0.050)</td><td>0.104<br>(0.072)</td></tr><tr><td>Finance experts Dummy</td><td>3.178<sup>**</sup><br>(1.459)</td><td>-0.822<br>(2.691)</td></tr><tr><td>Financial literacy</td><td>-1.521<sup>***</sup><br>(0.573)</td><td>2.640<sup>***</sup><br>(1.010)</td></tr><tr><td>Large Sample Dummy</td><td>-0.283<br>(0.948)</td><td>0.422<br>(1.573)</td></tr><tr><td>Vivid Sample Dummy</td><td>1.556<br>(0.960)</td><td>0.414<br>(1.610)</td></tr><tr><td>Age</td><td>-0.024<br>(0.041)</td><td>-0.086<br>(0.053)</td></tr><tr><td>Male Dummy</td><td>-1.846<sup>*</sup><br>(1.065)</td><td>2.711<sup>*</sup><br>(1.590)</td></tr><tr><td>Response time</td><td>-0.002<sup>**</sup><br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Student Dummy</td><td>-0.336<br>(1.546)</td><td>0.123<br>(2.244)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td>1.124<br>(1.417)</td><td>-1.902<br>(2.176)</td></tr><tr><td>Field of study (math) Dummy</td><td>0.577<br>(1.180)</td><td>2.129<br>(2.036)</td></tr><tr><td>Postgraduate education Dummy</td><td>-1.526<br>(1.222)</td><td>2.058<br>(1.983)</td></tr><tr><td>Risk appetite</td><td>0.353<sup>*</sup><br>(0.207)</td><td>0.023<br>(0.341)</td></tr><tr><td>Holding financial assets Dummy</td><td>0.648<br>(0.957)</td><td>-2.565<sup>*</sup><br>(1.417)</td></tr><tr><td>Constant</td><td>5.265<sup>*</sup><br>(3.145)</td><td>-6.680<br>(5.658)</td></tr><tr><td>R<sup>2</sup></td><td>0.29</td><td>0.12</td></tr><tr><td>N</td><td>670</td><td>670</td></tr></tbody></table>

Notes: Gap is the difference between the signal (3%) and the prior disaster prediction. Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Update in disaster prediction (Stage 3) is the difference between a participant's disaster prediction after the treatment (Decision 4), winsorized at the thresholds given by the 2.5th and 97.5th percentiles on the prior predictions, and the same prediction before the treatment (Decision 1) on trimmed data at the 2.5th and 97.5th percentiles. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** $p < 0.01$, ** $p < 0.05$, * $p < 0.1$)
F Additional tests of Hypotheses 2c and 3b

<table><caption>Table A.F.0.1: Additional linear regressions for assessing the impact of being a finance expert and information precision (Large Sample treatment) on the magnitude of decision updates.</caption><thead><tr><th></th><th>(1)<br>Absolute<br>Update<br>Disaster<br>prediction</th><th>(2)<br>Absolute<br>Update<br>Risky<br>share</th><th>(3)<br>Absolute<br>Update<br>Confidence<br>interval</th></tr></thead><tbody><tr><td>Finance experts Dummy</td><td>-0.423<br>(1.570)</td><td>1.945<br>(2.396)</td><td>2.579**<br>(1.235)</td></tr><tr><td>Large Sample Dummy</td><td>1.210<br>(1.431)</td><td>-0.898<br>(1.643)</td><td>-1.310<br>(1.301)</td></tr><tr><td>Finance experts × Large Sample Dummy</td><td>-1.132<br>(1.854)</td><td>-0.610<br>(2.259)</td><td>2.161<br>(1.527)</td></tr><tr><td>Vivid Sample Dummy</td><td>-0.142<br>(1.063)</td><td>-1.199<br>(1.412)</td><td>0.492<br>(0.756)</td></tr><tr><td>Male Dummy</td><td>-1.349<br>(1.016)</td><td>-2.256*<br>(1.354)</td><td>-0.832<br>(0.790)</td></tr><tr><td>Response time</td><td>-0.002***<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Student Dummy</td><td>3.650**<br>(1.809)</td><td>0.442<br>(1.778)</td><td>-1.212<br>(1.161)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td>-0.464<br>(1.422)</td><td>-1.314<br>(2.127)</td><td>-1.048<br>(1.184)</td></tr><tr><td>Field of study (math) Dummy</td><td>-0.433<br>(1.305)</td><td>2.607<br>(1.853)</td><td>0.219<br>(0.910)</td></tr><tr><td>Postgraduate education Dummy</td><td>-0.285<br>(1.262)</td><td>-0.608<br>(1.755)</td><td>-1.047<br>(1.047)</td></tr><tr><td>Risk appetite</td><td>-0.177<br>(0.227)</td><td>0.335<br>(0.278)</td><td>0.123<br>(0.209)</td></tr><tr><td>Holding financial assets Dummy</td><td>-0.222<br>(1.132)</td><td>-2.960**<br>(1.270)</td><td>-0.018<br>(0.990)</td></tr><tr><td>Constant</td><td>11.566***<br>(1.558)</td><td>10.025***<br>(2.322)</td><td>-4.690***<br>(1.373)</td></tr><tr><td>R<sup>2</sup></td><td>0.04</td><td>0.02</td><td>0.03</td></tr><tr><td>N</td><td>704</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. Finance experts × Large Sample Dummy takes value one when a participant is a finance expert and receives the Large Sample treatment. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
<table><caption>Table A.F.0.2: Additional linear regressions for estimating updates of disaster predictions and risky shares as a function of the interaction between the type of population and the vividness of information provision.</caption><thead><tr><th></th><th>(1)<br>Update<br>Disaster prediction<br>(Stage 3)</th><th>(2)<br>Update<br>Risky share<br>(Stage 3)</th><th>(3)<br>Update<br>Confidence interval<br>(Stage 3)</th><th>(4)<br>Update<br>Confidence interval<br>(Stage 3)</th></tr></thead><tbody><tr><td>Disaster prediction (Stage 1)</td><td></td><td></td><td>0.057<br>(0.035)</td><td></td></tr><tr><td>Confidence interval (Stage 1)</td><td></td><td></td><td>-0.330***<br>(0.068)</td><td></td></tr><tr><td>Finance experts Dummy</td><td>3.351**<br>(1.698)</td><td>-1.534<br>(2.677)</td><td>3.933***<br>(1.129)</td><td>4.031***<br>(1.324)</td></tr><tr><td>Large Sample Dummy</td><td>-0.546<br>(1.197)</td><td>1.249<br>(1.566)</td><td>0.249<br>(0.718)</td><td>-0.263<br>(0.891)</td></tr><tr><td>Vivid Sample Dummy</td><td>2.743*<br>(1.550)</td><td>0.110<br>(1.901)</td><td>1.806*<br>(0.951)</td><td>1.531<br>(1.049)</td></tr><tr><td>Finance experts × Vivid Sample Dummy</td><td>-2.643<br>(2.119)</td><td>0.329<br>(2.670)</td><td>-2.136*<br>(1.218)</td><td>-2.073<br>(1.356)</td></tr><tr><td>Male Dummy</td><td>-2.281*<br>(1.187)</td><td>4.357***<br>(1.567)</td><td>-1.128<br>(0.722)</td><td>-0.804<br>(0.791)</td></tr><tr><td>Response time</td><td>-0.000<br>(0.001)</td><td>0.002<br>(0.001)</td><td>-0.000<br>(0.000)</td><td>0.001<br>(0.001)</td></tr><tr><td>Student Dummy</td><td>-1.979<br>(2.070)</td><td>0.242<br>(2.157)</td><td>-1.133<br>(1.013)</td><td>-1.155<br>(1.161)</td></tr><tr><td>Field of study (economics or finance) Dummy</td><td>0.882<br>(1.630)</td><td>-1.447<br>(2.353)</td><td>-1.160<br>(0.981)</td><td>-1.050<br>(1.184)</td></tr><tr><td>Field of study (math) Dummy</td><td>0.652<br>(1.461)</td><td>2.035<br>(2.075)</td><td>-0.421<br>(0.785)</td><td>0.327<br>(0.921)</td></tr><tr><td>Postgraduate education Dummy</td><td>0.478<br>(1.463)</td><td>2.540<br>(2.073)</td><td>-1.464<br>(0.907)</td><td>-1.106<br>(1.048)</td></tr><tr><td>Risk appetite</td><td>0.412<br>(0.254)</td><td>-0.060<br>(0.329)</td><td>0.005<br>(0.171)</td><td>0.141<br>(0.211)</td></tr><tr><td>Holding financial assets Dummy</td><td>0.617<br>(1.269)</td><td>-1.969<br>(1.489)</td><td>0.380<br>(0.779)</td><td>-0.074<br>(1.002)</td></tr><tr><td>Financial literacy</td><td></td><td></td><td>-0.726<br>(0.487)</td><td></td></tr><tr><td>Constant</td><td>-8.578***<br>(1.889)</td><td>0.800<br>(2.734)</td><td>3.104<br>(2.231)</td><td>-5.452***<br>(1.454)</td></tr><tr><td>R<sup>2</sup></td><td>0.04</td><td>0.03</td><td>0.26</td><td>0.03</td></tr><tr><td>N</td><td>704</td><td>704</td><td>704</td><td>704</td></tr></tbody></table>

Notes: Disaster prediction (see Decision 1) is a participant's prediction (in %) of the past frequency of macroeconomic disasters. Confidence interval (see Decision 2) is an interval between 0 and 100 assessing a participant's confidence in their prediction. Large (Vivid) Sample Dummy takes value one if a participant was assigned to the Large (Vivid) Sample treatment. Financial literacy is the score between 0 and 5 on the 5-question financial literacy test. Finance experts (Male) [Study] Dummy takes value one when a participant is a finance expert (male) [Student]. Finance experts × Vivid Sample Dummy takes value one when a participant is a finance expert and receives the Vivid Sample treatment. The variable field of study (economics or finance) [math] takes value one if a person has studied or is studying a degree in economics or finance [mathematics, computer science, physics-chemistry, actuarial science or statistics]. Postgraduate education Dummy takes value one when a participant has completed a postgraduate degree. Holding financial assets Dummy takes value one when a participant holds financial assets. Risk appetite is the answer between 0 and 10 on the question regarding the extent to which a person is willing to take risks. Response time is the time (in seconds) participants took to complete the main tasks of the experiment (before the post-experimental survey that differed between experts and laypersons). Robust standard errors in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1)
# G Risk attitudes and cognitive ability

## G.1 Risk-neutrality

Using our 0-to-10 scale measuring willingness to take risks (Dohmen et al., 2011), we identify risk-neutral participants as those who chose the neutral response or the one immediately above or below (i.e., 4, 5, or 6), which corresponds to one-third of our sample (33.9%). In this sample of participants, we report, as for the whole sample, that both finance experts (22.7%) and non-experts (15.0%) significantly overestimated the prior probability of disaster risk of 3% (Sign Rank Tests, *p*-values < 0.001). Furthermore, the overestimation of disaster risk was significantly more pronounced for non-experts than for experts (Rank Sum Test, *p*-value = 0.002).

## G.2 Cognitive ability

Non-experts who achieved the maximum possible score on both the financial literacy and numeracy tests (14.0% of the sample) significantly overestimated disaster risk (with a mean prediction of 16.9%; Sign Rank Test, *p*-value < 0.001). Although we did not collect numeracy scores from finance experts, we can compare the two samples for those who achieved the maximum score on the financial literacy test, which represented 74.0% of finance experts and 21.0% of non-experts.<fnref n="19" /> Among these highly sophisticated respondents, we continue to observe an overestimation of disaster risk by both experts and non-experts (with mean predictions of 10.9% and 19.0%; Sign Rank Tests, *p*-values < 0.001), with the overestimation being significantly more pronounced among non-experts (Rank Sum Test, *p*-value = 0.002).

# H Preregistration debriefing report

The experiment with finance experts was preregistered at APredicted#124660, and the one with laypersons was preregistered at #143285. Except for the target population, the rest of the protocol was the same in both experiments.

To maintain clarity and focus, we have modified the structure of our hypotheses. In the paper, we

<footnote n="19">¹⁹In a representative sample of the U.S. adult population, only a quarter of respondents achieved the maximum score (Angrisani et al., 2020).</footnote>
organize our hypotheses around three model types (FIRE, information, and behavioral models) as we believe this improves readability and aligns better with the development of our hypotheses. We did not fully anticipate this writing approach at the time of our preregistration in the fall of 2023. However, we believe these adjustments make the paper clearer and easier to read. For transparency, we provide an explanation of the connection between our current hypotheses and the preregistered ones.

A starting hypothesis, which was omitted from our preregistration, concerns the study of prior predictions of disaster risk and investment levels. This is now captured in Hypothesis 1 (FIRE), which directly follows from classical models.

The other two hypotheses were preregistered, but as explained, we have restructured their order and wording for greater clarity. In the preregistration document, Hypotheses 1 and 4 correspond to our current Hypothesis 2a, while Hypothesis 2 is now described in Hypotheses 2b and 3a. Hypothesis 3 is now incorporated into Hypotheses 2c and 3b. Other potential individual differences (such as gender, age, and risk aversion) are not explicitly stated in our current hypotheses, unlike in the second sentence of preregistered Hypothesis 4. This is the case because, unlike financial literacy, these individual dimensions are not systematically varied by targeting a specific population, so we use these variables as controls.