|
on Nudge and Boosting |
|
Issue of 2025–12–08
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Ambec, Stefan; Andersson, Henrik; Cezera, Stéphane; Kanay, Ayşegül; Ouvrard, Benjamin; Panzone, Luca A.; Simon, Sebastian |
| Abstract: | What can be done to reduce the carbon footprint of consumption? To answer this, we conducted an online shopping experiment that tested the effects of two policy tools: a carbon tax (at two levels) and a behavioral nudge in the form of a traffic light-style label indicating a product’s carbon footprint (green for low, orange for medium, and red for high). To disentangle the tax’s substitution effect from its income effect, we held consumers’ purchasing power constant. We find that the tax alone significantly reduces the carbon footprint per euro spent but not per basket purchased, implying that the reduction is driven purely by the income effect. The label alone makes consumers buy fewer red products and more green products, although without reducing significantly their carbon footprint. We do find some substitution effect and a significant reduction of the carbon footprint per basket only when the tax is high enough and combined with the label. Next, we perform a welfare analysis grounded on a theoretical framework that accommodates for several assumptions about consumer’s preferences and motivations. We estimate the loss of consumer’s surplus from nudging consumers with the label. We also estimate the consumers’ valuation of a ton of CO2 avoided when they care about their climate impact. |
| Keywords: | Carbon tax; nudge; green label; carbon footprint; climate change; moral; behavior. |
| JEL: | D12 D90 H23 Q58 |
| Date: | 2025–12–02 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:131148 |
| By: | Riedmiller, Sebastian (Max Planck Institute for Research on Collective Goods); Sutter, Matthias (Max Planck Institute for Research on Collective Goods); Tonke, Sebastian (Max Planck Institute for Research on Collective Goods) |
| Abstract: | We provide a systematic framework to diagnose underlying problems and predict intervention effectiveness ex-ante. For this, we developed a parsimonious and generalizable survey tool (anamnesis). Our anamnesis classifies underlying problems along three fundamental diagnoses: awareness, intention, and implementation problems. We validate the framework in an online experiment with 7, 500 subjects. We find that (i) intervention effectiveness is heterogeneous across different settings, and (ii) our diagnosis accurately predicts this heterogeneity. On average, predicting a 10%-effect corresponds to an actual effectiveness of 8.92%. We further demonstrate the applicability of our framework to predict heterogeneities in the setting of COVID booster take-up. |
| Keywords: | context dependency, heterogeneous treatment effects, intervention design, experiment |
| JEL: | C93 D01 D61 D90 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18273 |
| By: | Johannes Hagen; Amedeus Malisa; Andrea Schneider; Jana Schuetz |
| Abstract: | We study the behavioral effects of a large-scale, repeated, and personalized reminder. Our empirical setting is Sweden’s annual pension statement, which is rolled out region by region to all working-age individuals. Combining this variation with unique individual-level user data from the national pension dashboard, we find strong and immediate effects. Dashboard users' likelihood of making a pension forecast rises by 28 percentage points in the statement week-a fourfold increase-before returning to baseline within three weeks. Remarkably, similar spikes occur each year, indicating that repeated reminders consistently reactivate attention rather than losing their impact over time. Complementary regional data on actual pension claims show a 33% surge in weekly claims during the week the statement is sent out. |
| Keywords: | repeated nudge, retirement planning, pension dashboard, pension information, digital engagement |
| JEL: | D83 H55 J32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12287 |
| By: | Scattarreggia, Emanuele |
| Abstract: | The digital economy is currently experiencing an unprecedented phase of transformation, driven by the relentless evolution of artificial intelligence (AI) and the Internet of Things (IoT). These technologies have transcended from being mere tools of convenience to becoming integral components of daily life, ushering in what can be described as the era of consumer cyborgification. This term captures the essence of how humans are increasingly merging with technology, not just physically through wearable devices but also in decision-making processes through AI-driven insights and nudges. As these technologies grow more sophisticated, they collect, analyse, and act upon enormous volumes of personal data. This capability, while beneficial in tailoring services and enhancing user experiences, simultaneously raises profound questions about privacy, autonomy, and consumer rights. The potential for misuse or overreach in data handling poses threats to individual privacy, while the autonomous decision-making aspects of AI challenge traditional notions of consumer autonomy. Yet, there is a silver lining. AI presents significant opportunities to guide consumers towards more informed, healthier, or economically beneficial choices. Through strategic nudging, AI can enhance consumer well-being, leading to a more efficient market where consumers are not only protected but empowered. However, the integration of such technologies calls for a re-evaluation of existing regulatory frameworks to ensure that they are fit for purpose in this new digital landscape. This paper delves into how current AI and consumer protection regulations can be adapted to meet these emerging challenges. The objective is to propose a framework where technological advancement and consumer protection can coexist synergistically. We aim to explore how laws can be refined to safeguard privacy and autonomy without stifling the innovation that drives economic and social benefits. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331306 |