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on Cognitive and Behavioural Economics |
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Issue of 2026–04–20
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Peter Andre (SAFE & Goethe University Frankfurt); Felix Chopra (Frankfurt School of Finance & Management); Luca Michels (University of Bonn); Johannes Wohlfart (University of Cologne & Max Planck Institute for Behavioral Economics Bonn) |
| Abstract: | Expectations are central to models of economic and financial decision-making. Yet in practice, individuals are often inattentive and, when asked, report fragile, context-dependent expectations that are only weakly linked to decisions. This raises the question to what extent they hold such expectations in the first place. Against this backdrop, we ask two questions: When people think about an economic issue, can they build on expectations they formed before? And does it matter if they cannot? We develop and validate a survey measure that distinguishes between individuals who can recall expectations formed in the past and those who must form expectations from scratch. We show that while many households have expectations about key economic variables, a large share of households do not — even among those close to decisions for which the expectation should be relevant. This matters: individuals without a previously-formed expectation (i) express expectations that are more context-dependent, (ii) update expectations more strongly but less persistently in response to new information, (iii) report expectations that are less relevant to decisions, and (iv) rely more on heuristics that do not require expectations when making economic decisions. |
| Keywords: | Expectations, Belief Formation, Previously-Formed, Context-Dependence, Learning, Decision Relevance, Heuristics |
| JEL: | C83 C91 D83 D84 D91 E71 G41 G53 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:402 |
| By: | Antal Ertl (ELTE Centre for Economic and Regional Studies; University of Iceland, School of Social Sciences); Dániel Horn (Corvinus University of Budapest; ELTE Centre for Economic and Regional Studies); Hubert János Kiss (ELTE Centre for Economic and Regional Studies; Corvinus University of Budapest) |
| Abstract: | We measured time, risk, social, and competitive preferences in a sample of 1, 035 secondary school students in Hungary. We examine whether meaningful groups of adolescents can be identified based on distinct constellations of these preferences, which we refer to as preference clusters. We also explore whether these clusters are associated with academic outcomes. Using cluster analysis, we consistently identify a group of students who are relatively more patient, less time-inconsistent, more risk-tolerant, more prosocial, and more cooperative. This preference cluster is positively associated with higher scores on standardized math and reading tests. Comparing cluster-based specifications to models with individual preferences entered separately, we find little loss in explanatory power, while preference clusters provide a more compact and interpretable description of how preferences are jointly organized within students. |
| Keywords: | economic preferences, preference profiles, cluster analysis, academic achievement, risk and time preferences, prosocial behavior |
| JEL: | C38 D91 I21 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:has:discpr:2605 |
| By: | Alex Farach; Alexia Cambon; Lev Tankelevitch; Connie Hsueh; Rebecca Janssen |
| Abstract: | Organizations have widely deployed generative AI tools, yet productivity gains remain uneven, suggesting that how people use AI matters as much as whether they have access. We conducted a field experiment with 388 employees at a Fortune 500 retailer to test two scaffolding interventions for human-AI collaboration. All participants had access to the same AI tool; we varied only the structure surrounding its use. A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use and substantially lower document production. A cognitive scaffolding intervention (partnership training that reframed AI as a thought partner) was associated with higher individual document quality at the top of the distribution. Treatment participants also showed greater positive belief change across the session, though sensitivity analyses suggest this likely reflects recovery from carry-over effects rather than genuine training-induced shifts. Both findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.08678 |
| By: | Yuriy Gorodnichenko; Marianna Kudlyak; Sophia Lobozynska; Iryna Skomorovych; Ulyana Vladychyn; Andriy Kovalyuk; Iryna Snovydovych |
| Abstract: | We elicit reservation wage premia for relocating to two Ukrainian cities, using a household survey conducted in mid-April to mid-July 2024 during the Russian invasion of Ukraine: high-risk Kharkiv (near the frontline) and moderate-risk Kyiv. Risk tolerance is a strong predictor of willingness to move to Kharkiv—the most risk-averse have roughly half the odds of the most risk-tolerant—but matters much less for Kyiv. This asymmetry is difficult to reconcile with the hypothesis that risk tolerance merely proxies for general mobility preferences. Separately estimating the elasticity of intertemporal substitution (EIS≈0.04), we find that including it renders risk tolerance insignificant for Kyiv but not for Kharkiv—a pattern illuminated by the Epstein-Zin separation of risk aversion and the EIS: risk aversion adds predictive power only when danger is high, while the EIS operates equally for both cities as a common relocation-cost channel. The very low EIS implies that relocation incentives structured as future benefits may be ineffective; front-loaded subsidies are more likely to influence behavior. |
| JEL: | D15 D81 J61 R23 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35072 |