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on Nudge and Boosting |
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Issue of 2026–07–27
three papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Giovanni Di Bartolomeo; Silvia Fedeli; Stefano Papa |
| Abstract: | We test whether minimal, non-informative messages can nudge tax compliance beyond standard deterrence. In a within-subjects lab experiment, we randomize exposure to either a reminder that leaves audit probability unchanged or an informative warningtied to higher audit probability, and estimate e¤ects on both the probability of evasion and the share of income evaded. A short non-informative reminder, holding incentives fixed, lowers the probability of evasion by about 16 percentage points, with no detectable effect on the evaded share among evaders; informative messages add at most marginal effects once audit probability is controlled for. |
| Keywords: | tax compliance; nudge; deterrence; audit; laboratory experiment |
| JEL: | H26 C91 D91 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:sap:wpaper:wp281 |
| By: | Kevin Bauer; Andreas Grunewald; Florian Hett; Johanna Jagow; Maximilian Speicher |
| Abstract: | We study how behavioral economics and machine learning can jointly construct effective treatment-targeting rules. In a large field experiment at an online fashion retailer with approximately 500, 000 consumers, we test a loss-framed discount message. We elicit individual loss aversion in a nested incentivized behavioral measurement experiment (N=582) and use machine learning to impute it from digital footprints. Targeting based on scaled behavioral measurement yields statistically significant revenue gains and outperforms causal forests. The results show how scaling behavioral measurement can improve algorithmic treatment assignment relative to purely data-driven approaches, especially when pilot data are unavailable, noisy, or costly. |
| Keywords: | treatment targeting, behavioral measurement, machine learning |
| JEL: | C93 C55 D91 M31 L81 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12772 |
| By: | Katharina Brütt (Vrije Universiteit Amsterdam); Eve Ernst (Vrije Universiteit Amsterdam) |
| Abstract: | Paternalistic policies are pervasive, yet little is known about how relationships between decision-makers and targets shape them. This paper examines how social proximity -- the degree to which individuals share identity-defining traits – influences paternalistic interventions. In an experiment with a representative U.S. sample, we manipulate proximity and distinguish between preference-responsive paternalism, reflecting one’s own preferences, and belief-responsive paternalism, reflecting beliefs about others’ preferences. Social proximity leaves the overall frequency of restrictions unchanged but shifts their driver: low proximity fosters preference-responsive paternalism, while high proximity promotes belief-responsive paternalism. Non-religious and independents restrict least; Christians and Republicans restrict more dissimilar others. |
| Keywords: | Paternalism, Social identity, Social proximity, Experiment |
| JEL: | C91 D12 D15 D91 |
| Date: | 2025–12–05 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250068 |