nep-nud New Economics Papers
on Nudge and Boosting
Issue of 2023‒11‒06
two papers chosen by
  1. Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal By Susan Athey; Niall Keleher; Jann Spiess
  2. Innovativeness, innovation adoption and priming: Nudging farmers in a large-scale randomized experiment in France By Douadia Bougherara; Lea Gosset; Raphaële Préget; Sophie Thoyer
  1. By: Susan Athey; Niall Keleher; Jann Spiess
    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:arx:papers:2310.08672&r=nud
  2. By: Douadia Bougherara (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier); Lea Gosset (UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Raphaële Préget (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier); Sophie Thoyer (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier)
    Abstract: This article is an empirical contribution on measuring farmers' ability to innovate, and on the effectiveness of a nudge-type non-monetary incentive on their (stated) intention to adopt an innovation such as the French "Label bas carbone", a voluntary scheme that certifies carbon credits. We propose an original methodology for measuring farmers' capacity to innovate ("innovativeness"), adapting the scale of Goldsmith and Hofacker (1991) to the specificities of farmers' decisions in a professional setting. Based on an online survey with more than 6, 000 responses from French farmers, we validate this scale and evaluate with a randomized experiment included in the questionnaire the net impact of a priming nudge targeting the most innovative farmers. The results indicate that the nudge tested has no significant or detectable impact on the surveyed sample, leading us to discuss the effectiveness of nudges when trying to influence high-stakes decisions.
    Keywords: Innovation, Carbon farming, Nudge, Behaviour, Experiment, Innovation -Carbon farming -Nudge -Behaviour -Experiment
    Date: 2023–08–29
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-04227775&r=nud
General information on the NEP project can be found at https://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.
This page is being maintained by Thomas Krichel. It was last updated on 2023‒12‒16 at 10:18.