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on Cognitive and Behavioural Economics |
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Issue of 2024–11–25
five papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Laura Breitkopf; Shyamal Chowdhury; Shambhavi Priyam; Hannah Schildberg-Hörisch; Matthias Sutter |
| Abstract: | We study the relationship between parenting style and a broad range of children’s skills and outcomes. Based on survey and experimental data from 5, 580 children and their parents, we find that children exposed to positive parenting have higher IQs, are more altruistic, open to new experiences, conscientious, and agreeable, have a higher locus of control, self-control, and self-esteem, perform better in scholarly achievement tests, behave more prosocially in everyday life, and are more satisfied with their life. Positive parenting is negatively associated with children’s neuroticism, patience, engagement in risky behaviors, and their emotional and behavioral problems. |
| Keywords: | parenting style, child outcomes, economic preferences, personality traits, IQ |
| JEL: | C91 D01 D10 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11391 |
| By: | Jian-Qiao Zhu; Joshua C. Peterson; Benjamin Enke; Thomas L. Griffiths |
| Abstract: | Understanding how people behave in strategic settings–where they make decisions based on their expectations about the behavior of others–is a longstanding problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90, 000 human decisions across more than 2, 400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people’s choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioural model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior. |
| Keywords: | behavioural game theory, large scale experiment, machine learning, behavioral economics, complexity |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11296 |
| By: | Guy Aridor; Rafael Jiménez-Durán; Ro'ee Levy; Lena Song |
| Abstract: | We provide a practical guide to designing, conducting, and analyzing experiments using social media platforms. First, we discuss the benefits and challenges of using the targeting capabilities of advertisements on social media to recruit participants for a large class of experiments. Next, we outline the different types of interventions and their advantages and disadvantages. Finally, we summarize available compliance and outcome data, as well as the main limitations and challenges involved in the design and analysis of social media experiments. Throughout, we provide technical details that are helpful when implementing these experiments. Overall, we argue that experiments on social media are powerful not only for studying economic issues around social media and online platforms but also for experiments studying economic behavior more broadly. |
| Keywords: | social media, experiments, digital interventions, subject recruitment, experiment design |
| JEL: | C90 C93 L82 L96 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11275 |
| By: | Weng, Hsu-Chi (Department of Real Estate and Construction Management, Royal Institute of Technology); Hermansson, Cecilia (Department of Real Estate and Construction Management, Royal Institute of Technology) |
| Abstract: | This paper examines how behavioral intention, combined with risk tolerance, self-defined financial confidence, and self-control, influences consumer credit usage. Grounded in the theory of planned behavior – which suggests that behavioral intention is the direct precursor to actual behavior – our study explores the moderating effects of risk tolerance, self-defined financial confidence, and self-control on behavioral intention to determine which individuals are more likely to utilize consumer credit among those intending to do so. Using a combination of survey and bank register data, we find that both higher risk tolerance and increased self-confidence are associated with a greater likelihood of taking on consumer credit, while self-control does not significantly moderate the relationship between behavioral intention and consumer credit behavior. Furthermore, we observe that gender differences in financial behavior are notable: men who report high confidence and an intention to use consumer credit tend to accrue more consumer credit, whereas higher self-control in men is linked to reduced credit use. Additionally, although both behavioral intention and higher income are positively associated with increased consumer credit use, stronger self-control in financial activities appears to mitigate this effect. Our study adds to consumer credit research by revealing the complex interplay between behavioral intention, risk tolerance, self-defined financial confidence, and self-control in consumer credit behavior. |
| Keywords: | consumer credit; behavioral intention; risk tolerance; financial confidence; self-control |
| JEL: | D12 D14 D91 G41 |
| Date: | 2024–10–29 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:kthrec:2024_008 |
| By: | Christina A. Martini; Björn Bos; Moritz A. Drupp; Jasper N. Meya; Martin F. Quaas |
| Abstract: | This paper investigates the link between dishonesty and the spread of COVID- 19 infections. In an online experiment and panel survey, 2, 723 Germans completed an incentivized coin-tossing task in March 2020 and reported their infection status in four subsequent survey waves up until December 2021. We find that individuals who are most likely dishonest in the coin-tossing task at the onset of the pandemic, as they report the highest number of winning coin tosses, are more than twice as likely to get a future COVID-19 infection than the sample mean. Respondents who are most likely to have reported dishonestly also engage more in behaviors that increase the risk of becoming infected and of transmitting the infection relative to likely honest respondents. Hence, we postulate that differences in preferences and norm compliance are underlying determinants that affect behavior in the experiment and in the field. We observe a similar relationship at the country level between an incentivized measure of civic honesty and excess deaths due to COVID-19 in 22 OECD countries. |
| Keywords: | dishonesty, Covid-19 infections, excess deaths, online experiment |
| JEL: | C90 I12 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11381 |