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on Experimental Economics |
| By: | Badini, Sofia (IIASA); Gehrke, Esther (Wageningen University); Lenel, Friederike (Potsdam Institute for Climate Impact Research); Schupp, Claudia (Technical University Munich) |
| Abstract: | We implement a randomized controlled trial in a low-income context to investigate whether students in lower-secondary school acquire information about potential career paths more effectively if this information is preceded by a task that allows students to explore their own interests and if the career information is ordered by the congruence between the careers and the student’s personality. We find that self-exploration in combination with the personalized display increases student information acquisition. Students also read about more diverse career paths and, low-performing students in particular, shift their focus from occupations that require university education towards those that require a high-school degree and are potentially more achievable. |
| Keywords: | information acquisition, career guidance, education, field experiment |
| JEL: | C93 D83 D91 I21 O15 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18490 |
| By: | Gallegos, Sebastian (Universidad Adolfo Ibañez) |
| Abstract: | This paper estimates the causal effect of a large language model–based study assistant on student behavior and learning outcomes in a natural field setting with real academic stakes. I design and deploy a course-specific AI assistant (GPT-UAI) for undergraduate econometrics and evaluate it through two randomized interventions implemented across seven coordinated course sections at a selective university in Chile. The first intervention targets the extensive margin of use, encouraging GPT-UAI adoption prior to the midterm exam. The encouragement raises the GPT’s awareness and reported usage, but does not change its perceived value and does not improve midterm performance. The second intervention targets use at the intensive margin, providing guidance on learning-oriented usage for the final exam. Guidance shifts interactions with GPT-UAI toward tutor-style engagement, increases perceived usefulness by 0.38 standard deviations, improves final-exam performance by 0.21 standard deviations, and raises the probability of earning a passing exam grade by 12 percentage points. The findings suggest that learning gains arise less from adoption than from guiding how students use course-specific AI assistants. |
| Keywords: | generative AI, large language models, higher education, field experiments, randomized controlled trials, student learning, human capital, AI-assisted learning, tutoring, technology in education |
| JEL: | I23 C93 O33 D83 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18513 |
| By: | Berlinski, Samuel (Inter-American Development Bank); Giannola, Michele (University of Naples Federico II, CSEF and the Institute for Fiscal Studies); Toppeta, Alessandro (SOFI, Stockholm University) |
| Abstract: | We study the relative effectiveness, cost-effectiveness, and interaction of family- and school -based learning interventions using a randomized controlled trial in Colombia that assigns children to a parental engagement program, a teacher professional development program, both, or a control group. Both interventions are grounded in a child-centered learning approach that emphasizes active engagement and the progression from informal to formal mathematical understanding. Each intervention independently generates sizable and statistically similar gains in early numeracy (0.17σ and 0.20σ). Combining them produces no additional learning gains, suggesting that the two interventions act as substitutes over the time horizon and skill domain we study. When benefits accruing to future cohorts are taken into account, the teacher development program becomes at least as cost-effective as, and potentially more cost-effective than, the parental engagement intervention. Our results suggest that, in this setting, strategically concentrating resources on a single binding constraint – either at home or in school – maximizes the short-run learning gains per dollar spent. |
| Keywords: | numeracy, childhood development, teacher development, parental engagement, randomized control trial, Colombia |
| JEL: | I21 I25 O15 J13 C93 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18485 |
| By: | van Loon, Austin; Kanopka, Klint (New York University) |
| Abstract: | Large language models (LLMs) have prompted proposals to replace human subjects in social science experiments with simulated responses. Empirical evaluations suggest that this practice---often called silicon sampling---can sometimes approximate human behavior but is unreliable. We delineate where this approach may still provide value and where it may not, but primarily study an alternative approach: one in which model-based predictions are used not as substitutes for human data, but as auxiliary measurements within randomized experiments. We formalize the inference of causal estimands from mixed-subjects randomized controlled trials, in which outcomes are observed for a subset of units while predictions are available for all units. Under transparent design conditions, we derive a family of estimators that remain unbiased for the average treatment effect in finite samples while exploiting predictions to reduce variance. We characterize when prediction-powered, calibration-based, arm-specifically tuned, and difference-in-predictions estimators improve precision, and we provide a software package which operationalizes these results and aids researchers to jointly select estimators and allocate budgets between human data collection and prediction generation. Together, our results show how generative artificial intelligence can improve experimental social science without compromising scientific validity. |
| Date: | 2026–04–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:y74mu_v1 |
| By: | ISHIKAWA, Takayuki; YOKOO, Hide-Fumi; KOBAYASHI, Yohei; TAKAHASHI, Kei; KANAI, Daiki; JOZUKA, Tatsuro; OHTAKE, Fumio |
| Keywords: | field experiment, green persuasion, nudge, online intervention, redelivery of package |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:hit:econdp:2026-02 |
| By: | Paul Bettega (Auteur indépendant); Paolo Crosetto (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Dimitri Dubois (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); Rustam Romaniuc (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School) |
| Abstract: | People use commitment devices to formalize and facilitate their goals. Self-commitments are ubiquitous and may take different forms: soft, when the commitment can be broken at a low cost, or hard, when that cost is high. The effects of soft and hard commitments have usually been studied separately. We conduct an online experiment with 1527 individuals representative of a big gambling company's client population to study the comparative effects of hard and soft commitment devices in a risk taking game. Our results show that asking for a hard limit leads subjects to reduce their risk-taking even when the limit turns out to be non-binding, i.e., the commitment is ex-post soft. Hard commitments lead to slightly lower levels of risk taking. |
| Keywords: | Risk taking, Soft commitment, Hard commitment, Self-control |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04193948 |
| By: | Yoichi SEKIZAWA |
| Abstract: | There are two elements that are crucial to building reliable evidence: (1) the existence of comparable treatment and control groups, and (2) the availability of measurable outcomes. In randomized controlled trials (RCTs), a simple coin toss can be used to assign units to treatment and control groups, thereby creating comparable groups. In encouragement designs, which are one form of RCT, individuals who are eligible for the intervention are randomly divided into an encouragement group and a control group, and only the encouragement group is actively encouraged to take up the intervention. In cluster RCTs, the unit of randomization is groups—such as classes, schools, or municipalities—so that one can implement RCTs even when individual-level randomization is difficult. Familiarity with statistical terms such as statistical significance and 95% confidence intervals makes it easier to understand the results of impact evaluations. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:eti:rpdpjp:26005 |
| By: | Roberto Brunetti (LEMMA - Laboratoire d'économie mathématique et de microéconomie appliquée - Université Paris-Panthéon-Assas, Université Paris-Panthéon-Assas); Matthieu Pourieux (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | This study leverages an online behavioural experiment to analyse whether politicians' decisions align with citizens' preferences and with citizens' decisions within the same decision environment. We recruited 760 local politicians and 655 non‐politicians in France to participate as policymakers in a taxation‐redistribution game. In the game, two policymakers compete to choose a flat tax rate for a group of citizens selected from the French general population. We manipulate (i) the information provided to policymakers about citizens' preferred tax rates, and (ii) the incentives associated with applying citizens' preferred tax rate. We also measure policymakers' beliefs regarding citizens' preferences. We observe that policymakers react positively to information, but they often deviate from it, which can be mostly explained by their beliefs. Incentivizing responsiveness has no impact on these results. This suggests that politicians trade off their own preferences about the policy outcome with an intrinsic motivation to implement citizens' preferences. Finally, we find that politicians believe that citizens want lower tax rates and are more confident in their beliefs than non‐politicians. Once beliefs are accounted for, we observe minor differences between the two samples. Our findings highlight the importance of politicians' beliefs and non‐financial motivations as determinants of their decisions. |
| Keywords: | representation, taxation-redistribution, politicians' behaviour, online experiment |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05577465 |
| By: | Marion Hoffman (IAST - Institute for Advanced Study in Toulouse); Tyler Thrash (Unknown); Christoph Hölscher (Unknown); Mubbasir Kapadia (Unknown); Victor R. Schinazi (Unknown) |
| Abstract: | Understanding crowd behavior is critical for designing buildings and public spaces with efficient circulation. However, the interplay of social and spatial contexts makes this endeavor challenging. This paper examines scenarios in which crowds perform a search task with time constraints, akin to individuals shopping or officers searching a crime area. We formulate and test two sets of hypotheses defined at the crowd and individual levels using desktop VR experiments. We conducted four experimental sessions that employed different social incentives (collaborative versus competitive) with a total of 140 participants, using a mixed factorial design where each individual participated in 12 trials. We found that competitive incentives produced higher levels of crowd aggregation than collaborative incentives. In addition, individuals were more likely to be influenced by others' behaviors in the collaborative compared to the competitive condition. Notably, these social signals were conveyed among participants without any verbal communication. We also developed a novel graph theoretic measure, "search attractiveness, " that accurately predicts space occupation during a search task. This paper highlights the roles of social and spatial contexts in understanding occupation and aggregation. |
| Keywords: | Crowd dynamics, Spatial layout, Virtual reality experiments, Graph theory, Space syntax |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05551130 |
| By: | Beaulieu-Guay, Louis-Robert; Béchard, Benoît; Ouimet, Mathieu; Claveau, François; Montpetit, Éric |
| Abstract: | Background: Various organizations expend resources to communicate the findings of systematic reviews to non-specialist audiences, such as policymakers and the general public. The Campbell Collaboration does so with its plain language summaries, which aim to communicate complex methodological information in an accessible manner. However, the effectiveness of such summaries in communicating the overall quality of the primary studies included in a systematic review remains understudied. Objectives: This study aims to assess how the design of a systematic review summary affects readers' perception of methodological quality of primary studies, their perception of the evidence's definitiveness and their attitudes towards the policy intervention discussed in the review. Data and Methods: Using a web-based experimental design, two studies were conducted to examine how various presentation formats, including the emphasis on methodological limitations and the use of images, influence readers’ perceptions of information presented in a systematic review summary. Results: Emphasizing methodological limitations significantly reduces readers' perception of the quality of the methods used in the studies included in a systematic review summary. Yet, highlighting methodological limitations does not influence people's perception of the definitiveness of the review findings or their attitude toward related policies. Furthermore, removing the images from the summary’s standard design appears to have minimal effect on how readers interpret information about methodological limitations. Conclusions: Modifications to the traditional summary design can amplify the awareness of laypeople and civil servants regarding methodological limitations in the scientific evidence. However, their way of processing information about these methodological limitations does not automatically propagate the change in attitude regarding methods to a change in attitudes regarding study results and policy options. |
| Date: | 2026–03–28 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:kwe94_v2 |
| By: | Nicolas Camilotto (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | This paper provides a life-cycle analysis of the Trust Game, using its trajectory as a lens to clarify the boundaries between experimental and behavioral economics. We first trace its 1995 creation by Berg et al. as a challenge to calculative trust paradigms. A bibliometric study then maps its diffusion, revealing two divergent paths in economics: one, rooted in experimental economics, prioritizes measurement; the other, in behavioral economics, theory-testing. These paths differ in methods and validity standards, constituting an epistemic divide that illuminates the fields’ evolving relationship. |
| Keywords: | trust; trust game; experimental economics; behavioral economics |
| JEL: | B2 B4 C9 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2026-11 |
| By: | Ying, Xiangji; Li, Tianjing; McKenzie, Joanne; Page, Matthew James (Monash University); Ninan, Kiran; Oberste, Jean-Pierre; Vorland, Colby J.; Brown, Andrew William (Indiana University School of Public Health-Bloomington); Qureshi, Riaz; DeVito, Nicholas J |
| Abstract: | Defining outcomes completely before conducting clinical trials helps to mitigate reporting biases; however, there is limited guidance to help investigators define outcomes completely. We aimed to develop a structured approach for defining trial outcomes completely and consistently. We reviewed literature, developed preliminary rules for defining outcomes, and refined them iteratively. We randomly selected randomized controlled trials (RCTs) on ClinicalTrials.gov that registered before their start dates and posted results by January 4, 2024. The 225 included RCTs evaluated 3, 424 outcomes. Two raters independently applied preliminary rules to define each outcome. When raters encountered outcomes they could not define, we refined the rules. We continued this process until no further changes were needed. We discussed and finalized our approach in a consensus meeting. We define an “outcome” as a value for each participant that will be used in analysis to generate study results. A complete outcome definition includes six elements: outcome domain, specific measurement, specific metric, cutoff, variable type, and timepoint. We developed rules for naming specific measurements for both subjective and objective outcomes. We expanded on prior work by developing more comprehensive categories for specific metrics. We introduced "cutoff" as a distinct element with three subelements. To clarify the boundary between outcome definitions and statistical methods, we replaced a previously described element, "method of aggregation, " with "variable type, " which refers to whether the value for each individual is continuous or categorical. Trialists and sponsors could use this approach alongside other guidelines to define outcomes in trial registrations, protocols, and result reports. |
| Date: | 2026–03–31 |
| URL: | https://d.repec.org/n?u=RePEc:osf:metaar:5wrsm_v1 |
| By: | Rustam Romaniuc (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School); Andrea Guido (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris); Pierre Baudry (Okoni, Paris); Cécile Bazart (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); Loïc Berger (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique, iRisk - Research Center on Risk and Uncertainty); Noémi Berlin (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique); Aurélie Bonein (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Imen Bouhlel (ESSEC Business School); Kene Boun My (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Michela Chessa (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur); Paolo Crosetto (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Etienne Dagorn (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, IEDES - Instutut d'Études du Développement de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne); Quentin David (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique); Etienne Farvaque (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique); Agnès Festré (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur); Abel François (EM Strasbourg - École de Management de Strasbourg = EM Strasbourg Business School - UNISTRA - Université de Strasbourg); Lisette Ibanez (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); Herrade Igersheim (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Nicolas Jacquemet (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Isabelle Lebon (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Mathieu Lefebvre (NukkAI [Paris]); Olivier L’haridon (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Danlin Li (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, Rennes SB - Rennes School of Business); Youenn Loheac (Rennes SB - Rennes School of Business); Stéphane Luchini (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Laurent Muller (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Matthieu Pourieux (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Elven Priour (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Sébastien Roussel (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, EPSYLON - Dynamique des capacités humaines et des conduites de santé - UMPV - Université de Montpellier Paul-Valéry); Petros Sekeris (TBS - Toulouse Business School); Maïté Stéphan (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Eli Spiegelman (CEREN - Centre de Recherche sur l'ENtreprise [Dijon] - BSB - Burgundy School of Business (BSB) - Ecole Supérieure de Commerce de Dijon Bourgogne (ESC)); Angela Sutan (ESSEC Business School); Uyanga Turmunkh (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique, IESEG School of Management Lille); Laurence Vardaxoglou (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Marc Willinger (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); Dimitri Dubois (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: | There is a significant gap in turnout between young people and older voters. The failure to instill a voting habit at an early age may have long term consequences in terms of future political participation as well as on other civic behaviors. Using a pre-registered online experiment with 3, 790 subjects, we implemented behavioral interventions aiming to stimulate youth turnout in the 2022 French presidential election. We also provide evidence on the effect of one behavioral intervention on youth turnout in a less salient election, the French legislative election that took place two months after the Presidential one. The results from the two experiments show the absence of any differences in turnout between the baseline and the treatment conditions. We investigate several mechanisms that can explain our results. |
| Keywords: | Field experiment, Behavioral public policy, Behavioral nudges, Youth turnout |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04677596 |
| By: | Florence Euzéby (NUDD - Usages du Numérique pour le Développement Durable - ULR - La Rochelle Université, ULR - La Rochelle Université); Sarah Machat (NUDD - Usages du Numérique pour le Développement Durable - ULR - La Rochelle Université); Juliette Passebois-Ducros (IRGO - Institut de Recherche en Gestion des Organisations - UB - Université de Bordeaux - Institut d'Administration des Entreprises (IAE) - Bordeaux) |
| Abstract: | Social media influencers express opinions on products or brands, either through commercial partnerships or in the form of unbiased content, called genuine content. To differentiate between sponsored and genuine content, influencers are increasingly choosing to include an impartiality disclosure: "This post is not sponsored." This study analyzes the combined influence of impartiality disclosure and influencer type on consumer intentions. An online experiment involving 533 participants was conducted. The findings indicate that an impartiality disclosure enhances behavioral intentions by reducing persuasion knowledge activation and increasing influencer credibility. Importantly, the positive impact is more pronounced for nano compared to micro influencers. |
| Date: | 2025–05–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05577140 |
| By: | Brenøe, Anne (University of Zurich); Stearns, Jenna (University of California, Davis); Martin, Richard (University of Bristol) |
| Abstract: | Using data from the only large-scale randomized controlled trial promoting prolonged exclusive breastfeeding, we study how the intervention affected child health and why. The intervention increased weight-for-age in infancy, with effects persisting through adolescence. We show that treated infants were breastfed more and received less water, juice, and other liquids, resulting in a more calorie-dense diet. A mediation analysis indicates that increased caloric intake explains a large share of the early weight gain, while reduced illness explains little. These findings suggest that, in this setting, the main benefits of breastfeeding promotion for physical growth came from improved nutrition. More broadly, the results highlight that the effects of breastfeeding promotion depend on the local alternatives to breast milk and may differ in settings where infant formula or other more nutritious substitutes are the main alternative. |
| Keywords: | breastfeeding, infant feeding, child health, the Promotion of Breastfeeding Intervention Trial (PROBIT) |
| JEL: | I10 J13 J24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18495 |
| By: | Zoé Burtschell (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur) |
| Date: | 2025–06–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05568375 |
| By: | Benjamin R. Handel; Louis-Jonas Heizlsperger; Jonas Knecht; Jonathan T. Kolstad; Ulrike Malmendier; Filip Matějka |
| Abstract: | We study how situational fluctuations in cognitive capacity shape behavior in high-stakes, real-time decision-making. Drawing on recent advances in behavioral economics that revolve around inattention, cognition and complexity, we show that cognitive load influences how physicians in emergency departments allocate mental effort and attention when making diagnostic and treatment decisions. We use quasi-random variation in patient-physician pairings, along with granular electronic medical record and audit-log data from many clinical interactions, to show that, under higher cognitive load, physicians substitute mental deliberation with more numerous but less precise diagnostic actions. Specifically, we document that higher load (i) increases the total number of orders of diagnostic tests (ii) reduces the use of targeted, but more uncommon tests (iii) increases the use of common tests and (iv) increases uncertainty in diagnostic beliefs. Cognitive load impacts downstream inpatient admission from the emergency department: a physician in the highest cognitive load decile increases admissions by 28% relative to the same physician in the lowest cognitive load decile, for the exact same kind of patient. These results offer novel field-based evidence on the dynamics of attention and belief formation, and shed light on how cognitive constraints shape diagnostic behavior in complex, real-world environments. |
| JEL: | D83 D91 I11 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35034 |
| By: | Shim, Myungkyu (Yonsei University); Kim, Kwang Hwan (Yonsei University); Lee, Myunghwan (New York University); Choi, Sangyup (Yonsei University); Bae, Siye (Northwestern University); Coibion, Olivier (University of Texas, Austin); Gorodnichenko, Yuriy (University of California, Berkeley) |
| Abstract: | We provide experimental evidence on how fiscal news shapes households’ expectations and spending behavior. Using a new survey of ~11, 000 Korean individuals linked to automatically collected high-frequency spending data, we elicit respondents’ fiscal and macroeconomic beliefs and randomly provide them with one of five pieces of information about current public debt levels, fiscal deficits, and the government’s plans for deficit reduction. Exogenous increases in expected future public debt raise expected inflation and increase consumer spending. On the other hand, exogenous increases in expected fiscal balance raise expected output growth and have no significant effect on consumer spending. |
| Keywords: | RCT, government debt, fiscal policy, expectations, transaction-level data, consumption |
| JEL: | E21 E3 E62 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18486 |
| By: | Chun Pang Chow; Hiroyuki Kasahara |
| Abstract: | Under heterogeneous treatment effects, the GMM weighting matrix in overidentified IV models dictates the estimand. We show that efficient GMM downeights high-variance instruments and frequently assigning negative weights that undermine causal interpretation. Moreover, GMM cannot simultaneously achieve efficiency and accommodate researcher-specified weights. We resolve this trade-off by developing the Representative Targeting (RT) estimator. By averaging instrument-specific Wald estimators under Positive Regression Dependence, RT ensures non-negative weights while achieving the semiparametric efficiency bound for its targeted estimand. We demonstrate the heterogeneity penalty empirically in a class-size experiment and apply RT to recover the Policy-Relevant Treatment Effect within a patent leniency design. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.07131 |
| 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); Léa 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 measures farmers' innovativeness and the effectiveness of a priming nudge on their (stated) intention to adopt an innovation, namely the French "Low-carbon label" (LCL). The LCL is an innovative certification framework that provides farmers with a potential new "green business model, " enabling them to generate income through the sale of certified carbon credits earned by reducing their own greenhouse gas (GHG) emissions. Using 6, 005 responses to an online survey with French farmers, we validate an original scale designed to measure farmers' capacity to innovate and find that innovativeness is positively correlated with stated intention to adopt the LCL. We then evaluate with a randomized experiment included in the questionnaire the net impact of a priming nudge, defined as exposure to a lexical field designed to unconsciously activate psychological factors, and implemented here with references to innovation in order to target the most innovative farmers. We show that the nudge has no detectable impact on the surveyed sample: it neither increases adoption intentions among the most innovative farmers nor discourages the less innovative ones. This absence of effect leads us to discuss the effectiveness of nudges when trying to influence farmers' high-stakes decisions.www.agropolis-fondation.fr/?lang=en. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the |
| Date: | 2026–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05571841 |
| By: | Luck, Nathalie (University of Passau and TU Munich); Grimm, Michael (University of Passau); Tamtomo, Kristian (Universitas Atma Jaya Yogyakarta) |
| Abstract: | Most impact assessments of agricultural training evaluate one-time interventions over short time horizons. However, farmers may initially show enthusiasm for a new technology but subsequently dis-adopt it after a trial period, while others may adopt practices gradually over time. This study investigates the causal impact of repeated agricultural training on the adoption of organic farming practices among Indonesian smallholder farmers. Using a randomized controlled trial and four waves of panel data spanning five years, we analyze adoption dynamics over time. Farmers in the treatment group received training twice, once in 2018 and again in 2022. Our findings show that repeated training significantly increased the adoption of organic farming practices, but no evidence that training motivated farmers to fully transition to organic farming. Adoption patterns reveal substantial dis-adoption, re-adoption, and late adoption following repeated training. The results contribute to understanding longer-term adoption dynamics after extension programs and provide insights into the challenges faced by smallholder farmers transitioning to sustainable agricultural practices. |
| Keywords: | organic farming, training, skills, technology adoption, information constraints, extension services, Indonesia |
| JEL: | C93 J24 J43 O12 O13 Q12 Q15 Q16 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18479 |
| By: | Prosanta Mandal; Arunava Patra; Sagar Chakraborty |
| Abstract: | Repeated interactions are ubiquitous and known to promote social behaviour. While research often focuses on cooperation in the Prisoner's Dilemma, experimental evidence suggests repeated interactions also foster fairness. This study addresses a gap in the literature by theoretically modelling the evolution of fairness within a repeated mini-ultimatum game. Specifically, we construct a repeated-game framework where offerers and accepters interact using reactive strategies. We then investigate whether fair reactive strategy pairs are resilient against unfair mutants in a two-species population. By analyzing short-term evolutionary stability via the concept of two-species evolutionary stable strategy, we identify a critical effective game length: below this value, fairness is promoted by offerers and accepters who comply with their partner's past actions. Above this critical value, fairness is maintained by `complier' offerers and fair accepters. We also show that specific reactive strategies effectively facilitate the emergence and sustenance of fairness in long-term mutation-selection dynamics. To this end, we develop a two-population stochastic dynamics model -- a generalization of classical adaptive dynamics -- that accounts for finite population sizes and non-local mutants in the reactive strategy space. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.03625 |
| By: | Zhenyu Gao; Wenxi Jiang; Yutong Yan |
| Abstract: | Prior research shows that large language models (LLMs) exhibit systematic extrapolation bias when forming predictions from both experimental and real-world data, and that prompt-based approaches appear limited in alleviating this bias. We propose a supervised fine-tuning (SFT) approach that uses Low-Rank Adaptation (LoRA) to train off-the-shelf LLMs on instruction datasets constructed from rational benchmark forecasts. By intervening at the parameter level, SFT changes how LLMs map observed information into forecasts and thereby mitigates extrapolation bias. We evaluate the fine-tuned model in two settings: controlled forecasting experiments and cross-sectional stock return prediction. In both settings, fine-tuning corrects the extrapolative bias out-of-sample, establishing a low-cost and generalizable method for debiasing LLMs. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02921 |
| By: | Dykstra, Holly (University of Konstanz); Fernández Guerrico, Sofía (University of Konstanz) |
| Abstract: | Income-based rents in public housing create an earnings disincentive. We collaborate with a public housing authority to design a behaviorally informed program that returns part of the rent induced by higher earnings to residents. Importantly, the program automatically enrolled households and was explicitly designed to make the increased payoff to working salient. Using a difference-in-differences approach, we estimate that annual household-head earnings rise 17% ($1, 370/year) and public assistance falls 7.5%, with impacts on both intensive and extensive margins. These results provide evidence that an in-work benefit designed for salience can offset the earnings disincentive and affect follow-through labor market behavior. |
| Keywords: | labor supply, in-work benefits, salience, public housing |
| JEL: | D91 I38 J22 R38 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18483 |
| By: | Federico Echenique; Gerelt Tserenjigmid |
| Abstract: | McGranaghan, Nielsen, O'Donoghue, Somerville, and Sprenger [2024] argue that standard paired choice tests for the common ratio effect are structurally biased when choice is stochastic, proposing valuation tests as a robust alternative. Using valuation tests, they find no systematic evidence for the common ratio effect, seemingly overturning much of the extant literature. We evaluate this conclusion in light of stochastic choice theory. We demonstrate that valuation tests are inherently biased and lack predictive power under standard expected utility assumptions. In contrast, we advocate for a ``strong'' paired choice test, proving it remains robustly unbiased across standard models of stochastic choice. Applying this strong test to existing experimental data, we find that the common ratio effect remains highly prevalent. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.06050 |