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on Experimental Economics |
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Issue of 2023‒11‒27
twenty-six papers chosen by |
| By: | Claudia Goldin |
| Abstract: | Harvard University Professor Claudia Goldin explained how she worked to eliminate 'informational barriers' to the economics field. |
| Keywords: | women in economics |
| Date: | 2022–10–25 |
| URL: | http://d.repec.org/n?u=RePEc:fip:l00001:94973&r=exp |
| By: | Przepiorka, Wojtek (Utrecht University) |
| Abstract: | Laboratory experiments belong in the methodical tool box of modern sociological scholarship. This chapter reviews the main points that researchers wanting to conduct a computerized, behavioral laboratory experiment should take into consideration. In particular, the chapter discusses (1) the organization of experimental sessions, (2) the set-up of experimental conditions, (3) the recruitment of study participants, (4) the presentation of experimental instructions, (5) the use of deception, (6) the design of decision and information feedback screens, (7) the implementation of the interaction situation, and (8) the employment of participant compensation. The chapter concludes with remarks on how lab experimental research in sociology can be planned, written up and published. |
| Date: | 2023–11–02 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:9cxq2&r=exp |
| By: | Francesco Bogliacino (Università di Bergamo); Paolo Buonanno (Università di Bergamo); Francesco Fallucchi (Università di Bergamo); Marcello Puca (Università di Bergamo, CSEF and Webster University Geneva) |
| Abstract: | In an online, pre-registered experiment, we explore the impact of AI mediated communication within the context of a Trust Game with unverifiable actions. We compare a baseline treatment, where no communication is allowed, to treatments where participants can use free-form communication or have the additional option of using ChatGPT-generated promises, which were assessed in a companion experiment. We confirm previous observations that communication bolsters trust and trustworthiness. In the AI treatment, trustworthiness sees the most significant increase, yet trust levels decline for those who opt not to write a message. AI-generated promises become more frequent but garner less trust. Consequently, the overall trust and efficiency levels in the AI treatment align with that of human communication. Contrary to our assumptions, less trustworthy individuals do not show a higher propensity to delegate messages to ChatGPT. |
| Keywords: | Artificial Intelligence, Trust Game, ChatGPT, Experiment. |
| JEL: | C93 D83 D84 D91 |
| Date: | 2023–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:sef:csefwp:689&r=exp |
| By: | Miguel A. Fonseca (Department of Economics, University of Exeter); Ashley McCrea (Department of Economics, University of Exeter) |
| Abstract: | In labour markets, women are often underrepresented relative to men. This underrepresentation may be due to inaccurate beliefs about ability across genders. Inaccurate beliefs might cause a sampling problem: to have accurate beliefs about a group, one must first collect information about that group. However, inaccurate beliefs may persist due to biased belief updating. We run a stylized hiring experiment to disentangle these two effects. We ask participants to create shortlists from a male and a female pool of workers and give them feedback on the skill of those they shortlist. Based on that information, participants hire workers, and provide us with their beliefs about the distribution of skills in the male and female pots. We study how recruiters update their beliefs as a function of their past shortlisting behaviour, and how they shortlist given their beliefs. As expected, participants were more likely to sample from the pool with the highest subjective mean quality (on average men) and lowest subject variance. Participants were not Bayesian updaters but there were no gender-specific biases in updating. Sampling more from a pool and, somewhat surprisingly, greater time spent engaging in sampling behaviour yield more accurate beliefs. |
| Keywords: | inaccurate statistical discrimination, belief updating, gender, shortlisting, chess |
| JEL: | C91 D83 J71 J78 M51 |
| Date: | 2023–11–07 |
| URL: | http://d.repec.org/n?u=RePEc:exe:wpaper:2315&r=exp |
| By: | Bruhin, Adrian; Petros, Fidel; Santos-Pinto, Luís |
| Abstract: | Teamwork has become increasingly important in modern organizations and the labor market. Yet little is known about the role of self-confidence in teamwork. In this paper, we present evidence from a laboratory experiment using a team effort task. Effort and ability are complements and there are synergies between teammates' efforts. We exogenously manipulate subjects' self-confidence in their ability using easy and hard general knowledge quizzes. We find that overconfidence leads to more effort, less free riding, and higher team revenue. These findings suggest that organizations could improve team performance by hiring overconfident workers. |
| Keywords: | Teamwork, Self-Confidence, Effort, Free Riding |
| JEL: | C71 C92 D91 D83 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wzbmbh:279569&r=exp |
| By: | Dor Morag; George Loewenstein |
| Abstract: | While the significance of narrative thinking has been increasingly recognized by social scientists, very little empirical research has documented its consequences for economically significant outcomes. The current paper addresses this gap in one important domain: valuations. In three experiments, participants were given the opportunity to sell an item they owned (mug in Study 1, hat in studies 2 and 3) using an incentive-compatible procedure (multiple price list). Prior to making selling decisions, participants were randomly assigned to either a narrative treatment, in which they were asked to tell the story of their item, or a list treatment, in which they were asked to list the characteristics of their item. The narrative treatment led to significantly higher selling prices and increased rates of participants refusing all offered prices. We further explore potential mechanisms, and the impact of different types of narratives, by analyzing self-reported classifications of, and employing natural language processing techniques on, participants’ narratives. |
| Keywords: | decision-making, experimental, narratives, valuations, willingness to accept |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10714&r=exp |
| By: | Waverly Wei; Xinwei Ma; Jingshen Wang |
| Abstract: | Randomized experiments have been the gold standard for assessing the effectiveness of a treatment or policy. The classical complete randomization approach assigns treatments based on a prespecified probability and may lead to inefficient use of data. Adaptive experiments improve upon complete randomization by sequentially learning and updating treatment assignment probabilities. However, their application can also raise fairness and equity concerns, as assignment probabilities may vary drastically across groups of participants. Furthermore, when treatment is expected to be extremely beneficial to certain groups of participants, it is more appropriate to expose many of these participants to favorable treatment. In response to these challenges, we propose a fair adaptive experiment strategy that simultaneously enhances data use efficiency, achieves an envy-free treatment assignment guarantee, and improves the overall welfare of participants. An important feature of our proposed strategy is that we do not impose parametric modeling assumptions on the outcome variables, making it more versatile and applicable to a wider array of applications. Through our theoretical investigation, we characterize the convergence rate of the estimated treatment effects and the associated standard deviations at the group level and further prove that our adaptive treatment assignment algorithm, despite not having a closed-form expression, approaches the optimal allocation rule asymptotically. Our proof strategy takes into account the fact that the allocation decisions in our design depend on sequentially accumulated data, which poses a significant challenge in characterizing the properties and conducting statistical inference of our method. We further provide simulation evidence to showcase the performance of our fair adaptive experiment strategy. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.16290&r=exp |
| By: | Gary Charness; Yves Le Bihan; Marie Claire Villeval (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Improving cognitive function and reducing stress may yield important benefits to individuals' health and to society. We conduct an experiment involving a three-month within-firm training program based on the principles of mindfulness and positive psychology at three large companies. We find an improvement in the difference-indifferences across the training and control groups in all five non-incentivized measures and in seven of the eight incentivized tasks but only the non-incentivized measures and one of the incentivized measures reached a standard level of significance (above 5%), showing strong evidence of its impact on both reducing perceived stress and increasing self-reported cognitive flexibility and mindfulness. At the aggregate level, we identify an average treatment effect on the treated for the non-incentivized measures and some effect for the incentivized measures. Remarkably, the treatment effects persisted three months after the training sessions ended. Overall, mindfulness training seems to provide benefits for psychological and cognitive health in adults. |
| Keywords: | Mindfulness, Attention, Cognition, Stress, Lab-in-the-Field Experiment |
| Date: | 2023–08–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04199093&r=exp |
| By: | Marina Chugunova (Max Planck Institute for Innovation and Competition); Wolfgang Luhan (University of Portsmouth) |
| Abstract: | As technology-assisted decision-making is becoming more widespread, it is important to understand how the algorithmic nature of the decisionmaker affects how decisions are perceived by the affected people. We use a laboratory experiment to study the preference for human or algorithmic decision makers in re-distributive decisions. In particular, we consider whether algorithmic decision maker will be preferred because of its unbiasedness. Contrary to previous findings, the majority of participants (over 60%) prefer the algorithm as a decision maker over a human—but this is not driven by concerns over biased decisions. Yet, despite this preference, the decisions made by humans are regarded more favorably. Participants judge the decisions to be equally fair, but are nonetheless less satisfied with the AI decisions. Subjective ratings of the decisions are mainly driven by own material interests and fairness ideals. For the latter, players display remarkable flexibility: they tolerate any explainable deviation between the actual decision and their ideals, but react very strongly and negatively to redistribution decisions that do not fit any fairness ideals. Our results suggest that even in the realm of moral decisions algorithmic decision-makers might be preferred, but actual performance of the algorithm plays an important role in how the decisions are rated. |
| Keywords: | delegation; algorithm aversion; redistribution; fairness; |
| JEL: | C91 D31 D81 D9 O33 |
| Date: | 2023–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:439&r=exp |
| By: | Levely, Ian; van den Berg, Marrit |
| Abstract: | Through three related experiments with spouses in rural Tanzania, we show that intra-household bargaining can lead to inefficient outcomes, as spouses benefit more from private earnings and thus avoid joint projects. We randomly assign labor to spouses in a real-effort task, then observe how income is spent in a in a controlled setting. A spouse’s bargaining power increases only with income earned alone. Female subjects in particular avoid joint projects, even when doing so is costly to the household. Such choices are correlated with lower agricultural income outside the lab. Similar mental accounting and bargaining could explain inefficient intra-household decision-making in this and other settings, where there is a trade-off between maximizing individual and household income. |
| Date: | 2023–11–02 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:pkcqd&r=exp |
| By: | Fortuna Casoria (CEREN - Centre de Recherche sur l'ENtreprise [Dijon] - BSB - Burgundy School of Business (BSB) - Ecole Supérieure de Commerce de Dijon Bourgogne (ESC)); Fabio Galeotti; Marie Claire Villeval (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We combined a natural experiment (the occurrence of the COVID-19 pandemic in 2020) with the tools of laboratory experiments to study whether and how an unprecedented shock on social interactions (the introduction and abrogation of a nationwide lockdown) affected the evolution of individuals' social preferences, and willingness to trust others. In a longitudinal online incentivized experiment during the first lockdown in France, we elicited the same participants' preferences for prosociality, trust and trustworthiness every week for three months. Despite the exposure to long-lasting social distancing, prosocial preferences and the willingness to reciprocate the trust of others remained stable during the whole period under study. In contrast, the lockdown had an immediate negative effect on trust, which remained at lower levels til after the lifting of such measures but recovered its initial level nine months later. The decline in trust was mainly driven by individuals who experienced financial hardship, a lack of outward exposure, and higher anxiety during the lockdown. |
| Keywords: | Social preferences, Trust, Trustworthiness, Pandemic, COVID-19, Social distancing |
| Date: | 2023–02–05 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03974756&r=exp |
| By: | Abatemarco, Antonio; Cascavilla, Alessandro; Dell’Anno, Roberto; Morone, Andrea |
| Abstract: | Corruption is known to be one of the real life situations which may jeopardize the effectiveness of fines in deterring crime. We present a model of ‘crime with corruption’ by which both the dilution of crime deterrence due to corruption, as well as the possibility of crime encouraging fines are formally highlighted. More importantly, by running an experiment on a subject pool of students for the case of illegal waste disposal, we provide experimental evidence on the validity of our theoretical predictions. We find that increasing fine rate may become crime encouraging or at least ineffective, beyond a context-specific fine threshold. In a policy perspective, we suggest that the optimal design of a crime-deterring sanctioning system must simultaneously account for both corruption practices and anti-corruption policies. |
| Keywords: | corruption, crime, fine, waste |
| JEL: | C91 H10 K14 K42 Q50 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:118733&r=exp |
| By: | Daniela Sele (ETH); Marina Chugunova (Max Planck Institute for Innovation and Competition) |
| Abstract: | Are people algorithm averse, as some previous literature indicates? If so, can the retention of human oversight increase the uptake of algorithmic recommendations, and does keeping a human in the loop improve accuracy? Answers to these questions are of utmost importance given the fast-growing availability of algorithmic recommendations and current intense discussions about regulation of automated decision-making. In an online experiment, we find that 66% of participants prefer algorithmic to equally accurate human recommendations if the decision is delegated fully. This preference for algorithms increases by further 7 percentage points if participants are able to monitor and adjust the recommendations before the decision is made. In line with automation bias, participants adjust the recommendations that stem from an algorithm by less than those from another human. Importantly, participants are less likely to intervene with the least accurate recommendations and adjust them by less, raising concerns about the monitoring ability of a human in a Human-in-the-Loop system. Our results document a trade-off: while allowing people to adjust algorithmic recommendations increases their uptake, the adjustments made by the human monitors reduce the quality of final decisions. |
| Keywords: | automated decision-making; algorithm aversion; algorithm appreciation; automation bias; |
| JEL: | O33 C90 D90 |
| Date: | 2023–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:438&r=exp |
| By: | Xiaoxiao Hu; Haoran Lei |
| Abstract: | An expert seller chooses an experiment to influence a client's purchasing decision, but may manipulate the experiment result for personal gain. When credibility surpasses a critical threshold, the expert chooses a fully-revealing experiment and, if possible, manipulates the unfavorable result. In this case, a higher credibility strictly benefits the expert, whereas the client never benefits from the expert's services. We also discuss policies regarding monitoring expert's disclosure and price regulation. When prices are imposed exogenously, monitoring disclosure does not affect the client's highest equilibrium value. A lower price may harm the client when it discourages the expert from disclosing information. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.09544&r=exp |
| By: | Alix Rouillé (PhD student, CEPS, ENS Paris-Saclay) |
| Abstract: | The combination of social norms and nudges has proven to be a powerful tool for inciting people to adopt pro-environmental behaviors. In this study, we implemented nudges that promote pro-environmental behavior still not explored by behavioral economics: waste composting. In particular, we designed priming and social norm nudges to incite people looking for information about waste composting possibilities. We set up a field experiment with a two-fold purpose. First, remove the barriers towards collective composting in Lyon by using posters related to priming theory with QR Codes that redirect directly to the website of a local association dedicated to environmental actions. Second, these posters created new social norm mechanisms. Since composting is still practiced by only a minority of people in France, the standard way of combining nudges and social norms is insufficient in this context. Here, we focus on descriptive and injunctive norms with local dimensions. These new norms aimed to make the nudge more efficient by increasing the number of scans. We observed that the scans of the posters allowed for a significant increase in the visits to the website over several months, thus improving information about collective waste composting. Although no significant differences were found between social norms treatments, these results show that the QR Code is a promising tool for implementing nudges. |
| Keywords: | Nudge, composting, priming, social norms, QR Code |
| JEL: | C93 D91 Q53 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:fae:wpaper:2023.11&r=exp |
| By: | Ternovski, John; Keppeler, Florian; Jilke, Sebastian (Georgetown University); Vogel, Dominik (University of Hamburg) |
| Abstract: | Randomized Control Trials (RCTs) are increasingly relied upon by policymakers as part of efforts to incorporate evidence into the policymaking process, a movement known as evidence-based policymaking, or EBPM. Testing possible policy interventions via RCTs before full rollout is commonly thought to be the gold standard of evidence in the EBPM process. However, real-world policy changes do not always scale up as expected. Even large-N RCTs targeting a random sample of policy beneficiaries do not capture the influence of social networks and risk missing consequential spillover effects. We illustrate this issue by assessing the efficacy of monetary incentives to increase COVID-19 vaccination in an RCT over the entire population of a medium-sized European town (~40, 000 residents). We use administrative vaccination data as our primary outcome. Since the entire population was randomized, we are able to estimate spillover effects within households. There were significant negative spillover effects on booster vaccinations that we attribute to a displacement effect, potentially driven by long lines at the vaccination events. Our results illustrate that using a population-level RCT to test whether a policy scales can help avoid costly, ineffective, or even counterproductive policy outcomes. |
| Date: | 2023–10–25 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:k5uap&r=exp |
| By: | Johannes Abeler; David Huffman; Colin Raymond |
| Abstract: | Using field and laboratory experiments, we demonstrate that the complexity of incentive schemes and worker bounded rationality can affect effort provision, by shrouding attributes of the incentives. In our setting, complexity leads workers to over-provide effort relative to a fully rational benchmark, and improves efficiency. We identify con tract features, and facets of worker cognitive ability, that matter for shrouding. We find that even relatively small degrees of shrouding can cause large shifts in behavior. Our results illustrate important implications of complexity for designing and regulating workplace incentive contracts. |
| Date: | 2023–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:1012&r=exp |
| By: | Picard, Julien; Banerjee, Sanchayan |
| Abstract: | Fighting the climate crisis requires changing many aspects of our consumption habits. Previous studies show that a first climate-friendly action can lead to another. Does deciding not to eat meat increase our willingness to do more for the environment? Can encouraging vegetarianism alter this willingness? Using an online randomised control trial, we study the side effects of a social norm nudge promoting vegetarianism on environmental donations. We develop an experimental design to estimate these side effects and a utility maximisation framework to understand their mechanisms. Using an instrumental variable, we find that choosing not to eat meat increases donations to pro-environmental charities. We use machine learning to find that the social norm nudge crowds out donations from the population segment prone to choosing vegetarian food after seeing the nudge. However, the nudge led another group to make less carbon-intensive food choices without affecting their donations. Our results suggest that whilst social norm nudges are effective on specific population segments, they can also reduce the willingness of some groups to do more. |
| Keywords: | social norm; meat; climate change; behavioural spillovers; side effects |
| JEL: | C30 C93 D91 Z10 |
| Date: | 2023–09–13 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120566&r=exp |
| By: | Konstantinos Georgalos; Nathan Nabil |
| Abstract: | In this paper we aim to investigate how the complexity of a decision-task may change an agents strategic behaviour as a result of increased cognitive fatigue. In this framework, complexity is defined as a function of the number of outcomes in a lottery. Using Bayesian inference techniques, we quantitatively specify and estimate adaptive toolbox models of cognition, which we rigorously test against popular expectation based models; modified to account for complexity aversion. We find that for the majority of the subjects, a toolbox model performs best both in-sample, and with regards to its predictive capacity out-of-sample, suggesting that individuals result to heuristics when the complexity of a task overwhelms their cognitive load. |
| Keywords: | Complexity aversion, Toolbox models, Heuristics, Risky choice, Bayesian modelling |
| JEL: | C91 D91 D81 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:400814269&r=exp |
| By: | Christos Genakos; Costas Roumanias; Tommaso Valletti |
| Abstract: | We present novel evidence from a large panel of UK consumers who receive personalized reminders from a specialist price-comparison website about the precise amount they could save by switching to their best-suited alternative mobile telephony plan. We document three phenomena. First, even self-registered consumers with positive savings exhibit inertia. Second, we show that being informed about potential savings has a positive and significant effect on switching. Third, controlling for savings, the effect of incurring overage payments is significant and similar in magnitude to the effect of savings: paying an amount that exceeds the recurrent monthly fee weighs more on the switching decision than being informed that one can save that same amount by switching to a less inclusive plan. We interpret this asymmetric reaction on switching behavior as potential evidence of loss aversion. In other words, when facing complex and recurrent tariff plan choices, consumers care about savings but also seem to be willing to pay upfront fees in order to get "peace of mind". |
| Keywords: | tariff plan choice, inertia, switching, loss aversion, mobile telephony |
| Date: | 2023–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:cep:poidwp:079&r=exp |
| By: | Esponda, Ignacio; Vespa, Emanuel |
| Abstract: | Abstract: We present an experimental framework to study the extent to which failures of contingent thinking explain classic anomalies in a broad class of environments, including overbidding in auctions and the Ellsberg paradox. We study environments in which the subject’s choices affect payoffs only in some states, but not in others. We find that anomalies are in large part driven by incongruences between choices in the standard presentation of each problem and a ‘contingent’ presentation, which focuses the subject on the set of states where her actions matter. Additional evidence suggests that this phenomenon is in large part driven by people’s failure to put themselves in states that have not yet happened even though they are made aware that their actions only matter in those states. |
| Keywords: | Economics, Applied Economics, Applied economics, Econometrics, Economic theory |
| Date: | 2023–11–10 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:ucsdec:qt32j4d5z2&r=exp |
| By: | Lewis, Andrew; Vu, Patrick; Duch, Raymond (University of Oxford); Chowdhury, Areeq |
| Abstract: | The rapid advancement of ‘deepfake’ video technology — which uses deep learning artificial intelligence algorithms to create fake videos that look real — has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people’s alertness to and ability to detect a high-quality deepfake amongst a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compared to a control group who viewed only authentic videos (34.1%). Second, we find that when individuals are given a warning that at least one video in a set of five videos is a deepfake, only 21.6% of respondents correctly identify the deepfake as the only inauthentic video, while the remainder erroneously select at least one genuine video as a deepfake. |
| Date: | 2023–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:cb7rw&r=exp |
| By: | Guilherme Araújo Lima (UFMG); Igor Viveiros Melo Souza (UFMG); Mauro Sayar Ferreira (UFMG) |
| Abstract: | We conduct Monte Carlo experiments to evaluate the performance of different Difference-in-Differences estimators under treatment assignment mechanisms affected by shocks suffered by treated units and also in contexts where the treatment effect spills over to units in the control group. In particular, we compare the estimators proposed by Callaway and Sant'Anna (2021), Borusyak et al. (2021), and Sun and Abraham (2021), as well as the two-way fixed effects (TWFE) estimator. The results demonstrate that the treatment assignment mechanisms we design, and the presence of spillover effects can severely compromise the performance of the considered estimators, leading to bias and, even more importantly, inconsistency. Therefore, cautious for interpreting the results should be taken in applications where the environment studied resembles those we consider. The development of more robust estimators is a necessity and a prosperous research venue. |
| Keywords: | Difference-in-Differences; Causal Inference; Treatment assignment mechanisms; Spillover effects. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:cdp:texdis:td662&r=exp |
| By: | Alice Dominici and Lisen Arnheim Dahlström |
| Abstract: | We investigate tailoring information framing to recipients’ backgrounds to boost vaccination uptakes. 7616 Swedish mothers stratified by education and immigration background received a leaflet on their children’s upcoming HPV vaccination opportunity. The leaflet’s framing was randomized between emotional and scientific, with control units receiving an uninformative announcement. Mothers with compulsory schooling exposed to scientific framing increased their uptake by 5.7 percentage points (7.25%). The effect was driven by attentive readers with little previous HPV knowledge. Emotional framing decreased uptake by 4.8 percentage points (5.41%) among high school-educated mothers who read superficially and were more hesitant at baseline. |
| Keywords: | Information framing, Vaccinations, Education. |
| JEL: | I12 I18 D83 J13 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2023/02&r=exp |
| By: | Giulia Caprini |
| Abstract: | I study the non-verbal language of leading pictures in online news and its influence on readers’ opinions. I develop a visual vocabulary and use a dictionary approach to analyze around 300, 000 photos published in US news in 2020. I document that the visual language of US media is politically partisan and significantly polarised. I then demonstrate experimentally that the news’ partisan visual language is not merely distinctive of outlets’ ideological positions, but also promotes them among readers. In a survey experiment, identical articles with images of opposing partisanships induce different opinions, tilted towards the pictures’ ideological poles. Moreover, as readers react more to images aligned with their viewpoint, the news’ visual bias causes issue polarization to increase. Finally, I find that media can effectively slant readers using neutral texts and partisan pictures: this result calls for the inclusion of image scrutiny in news assessments and fact checking, today largely text-based. |
| Date: | 2023–05–03 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:1016&r=exp |
| By: | Konstantinos Georgalos; Nathan Nabil |
| Abstract: | In an attempt to elucidate the classic violations of expected utility theory, the behavioural economics literature heavily relies on the influential work of Tversky and Kahneman (1992), specifically the Cumulative Prospect Theory (CPT) model and the Heuristics-and-Biases program. While both approaches have significantly contributed to our understanding of decision-making under uncertainty, empirical evidence remains inconclusive. In this study, we investigate the performance of each approach across a wide range of choice environments and increasing cognitive load, encompassing gains, losses, time pressure, and complexity. Utilising data from various studies and employing Bayesian inference, we assess the performance of CPT in comparison to an adaptive cognitive toolbox model of heuristics. For subjects classified as toolbox decision makers, we examine the content (i.e., which heuristics) and the size of the toolbox (i.e., how many heuristics). Our findings reveal that as the choice environment objectively increases in complexity, individuals transition from using sophisticated expectation-based utility models to relying on a set of simplification heuristics for decision-making. We quantify the relationship between toolbox usage and complexity, showing a significant and positive correlation between the two. Furthermore, our results indicate that as task complexity rises, individuals tend to employ smaller toolboxes with fewer heuristics for decision-making. |
| Keywords: | Complexity, Toolbox models, Heuristics, Risky choice, Bayesian modelling |
| JEL: | C91 D81 D91 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:400814162&r=exp |