nep-nud New Economics Papers
on Nudge and Boosting
Issue of 2024‒03‒25
five papers chosen by



  1. Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal By Athey, Susan; Keleher, Niall; Spiess, Jann
  2. Digital Interventions and Habit Formation in Educational Technology By Agrawal, Keshav; Athey, Susan; Kanodia, Ayush; Palikot, Emil
  3. Eliciting Paternalistic Preferences: An Incentivised Experiment By Schütze, Tobias; Carlhoff, Henrik; Witschel, Helena
  4. Nudging by Beauty:Improving Women's Health Decisions and Well-Being in the Field By Hisaki KONO; Minhaj MAHMUD; Yasuyuki SAWADA; Nahoko MITSUYAMA; Tomomi TANAKA
  5. The right amount of information for environmental awareness? When the level of visual complexity helps motivate more responsible behavior By Ulysse Soulat

  1. By: Athey, Susan (Stanford U); Keleher, Niall (Innovations for Poverty Action, New Haven); Spiess, Jann (Stanford U)
    Abstract: In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53, 000 college students, where the goal was to use "nudges" to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.
    Date: 2023–10
    URL: http://d.repec.org/n?u=RePEc:ecl:stabus:4146&r=nud
  2. By: Agrawal, Keshav (Stanford U); Athey, Susan (Stanford U); Kanodia, Ayush (Stanford U); Palikot, Emil (Stanford U)
    Abstract: We evaluate a contest-based intervention intended to increase the usage of an educational app that helps children in India learn to read English. The evaluation included approximately 10, 000 children, of whom about half were randomly selected to enter a reading contest, whereby those children who ranked sufficiently high on a leaderboard were awarded a set of books. During 12 weeks after the contest, when the treatment group had no additional incentives to use the app, children in the treated group read 75% more stories than the control group, consistent with the formation of reading habits. Furthermore, post-contest, the treated group abandoned the app 6% less often than the control group. These results demonstrate that low-cost interventions have the potential to be used to instill reading habits in the digital context.
    Date: 2024–01
    URL: http://d.repec.org/n?u=RePEc:ecl:stabus:4147&r=nud
  3. By: Schütze, Tobias; Carlhoff, Henrik; Witschel, Helena
    Abstract: Individual paternalistic preferences are central to the question to what extent the state may intervene in the freedom of choice of its citizens. Albeit its practical and theoretical importance, there is yet no incentivised tool to elicit those preferences. In this paper, we present a simple and abstract experiment to elicit paternalistic preferences and also investigate its relationship with individual psychological constructs that are argued to correlate with paternalistic preferences. In line with previous empirical results, our experimental data suggest that paternalistic preferences are indeed heterogeneously distributed in our sample. Moreover, we identify outcome related and autonomy related motives as important factors of paternalistic preferences. More precisely, (especially young) individuals with a strong desire for autonomy are more likely to opt for an informed choice and individuals with a strong focus on the outcome are more likely to opt for an uninformed choice than giving up their autonomy.
    Keywords: paternalistic preferences, revealed preferences approach, psychology of decision making, autonomous decision making, paternalism, libertarianism
    JEL: C91 D91 H10
    Date: 2024
    URL: http://d.repec.org/n?u=RePEc:zbw:roswps:169&r=nud
  4. By: Hisaki KONO; Minhaj MAHMUD; Yasuyuki SAWADA; Nahoko MITSUYAMA; Tomomi TANAKA
    Abstract: Health interventions often fail to influence behavior because they overlook the choice architecture. We assess a unique intervention targeting women in rural Bangladesh, which emphasized health, hygiene, and nutrition’s role in skin beauty. This intervention aimed to attract the attention of women, who tend to be beauty-conscious. Using the high-dimensional covariate balancing propensity score method, we find significant impacts on beauty, health outcomes, social relationships, and subjective well-being. Our analysis suggests the intervention’s effectiveness is unlikely due to omitted variable bias. Using meta-analysis, we highlight its effectiveness in leveraging beauty salience compared with existing health and hygiene programs.
    Keywords: Hygiene, Health, Beauty.
    JEL: I1 D9
    URL: http://d.repec.org/n?u=RePEc:kue:epaper:e-23-009&r=nud
  5. By: Ulysse Soulat (NUDD - Usages du Numérique pour le Développement Durable - ULR - La Rochelle Université)
    Abstract: Making sustainable urban mobility more attractive through a self-tracking application could contribute to behavioral change. Marketing research reflects the importance of choosing the right information to convey in order to encourage responsible practices. In this study, we investigate the effect of visual complexity on people's intentions to reduce the carbon footprint of their journeys using a self-tracking application. An experiment involving 362 participants examines the impact of self-tracking on behavioral intentions through the prism of visual complexity. On the one hand, the experiment shows that moderately complex home pages have a positive effect on behavioral intentions. On the other hand, when subjects are subjected to a moderate level of visual complexity (vs. simple or complex), their sense of self-efficacy and intention to use are higher.
    Abstract: Rendre la mobilité urbaine durable plus attractive par une application de self-tracking pourrait contribuer au changement de comportement. Les recherches en marketing reflètent l'importance du choix de l'information à transmettre afin d'amener à des pratiques responsables. Dans cette recherche, nous étudions l'effet de la complexité visuelle sur les intentions de réduire l'empreinte carbone de ses déplacements grâce à une application de self-tracking. Une mise en situation auprès de 362 participants interroge le phénomène du self-tracking sur les intentions comportementales par le prisme de la complexité visuelle. D'une part, l'expérimentation conduite montre que les pages d'accueil de complexité modérée ont un effet positif sur les intentions comportementales. D'autre part, lorsque les sujets sont soumis à un niveau de complexité visuelle modéré (vs simple ou complexe), leur sentiment d'auto-efficacité et leur intention d'utilisation sont plus élevés.
    Keywords: Mobile application, self-efficacy, visual complexity, responsible behavior, self-tracking, Application mobile, auto-efficacité, complexité visuelle, comportement responsable
    Date: 2023–06–01
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-04454382&r=nud

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