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on Experimental Economics |
| By: | Belleville, Eric |
| Keywords: | Research Methods/Statistical Methods |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360820 |
| By: | Uwineza, Yvette |
| Keywords: | Community/Rural/Urban Development |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361132 |
| By: | De Marchi, Elisa |
| Keywords: | Labor and Human Capital |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361191 |
| By: | Daniel Banko-Ferran (University of Pittsburgh); Valeria Burdea (LMU Munich); Jonathan Woon (University of Pittsburgh) |
| Abstract: | This study evaluates the effectiveness of three widely used belief elicitation methods in an online setting: the binarized scoring rule (BSR), the stochastic Becker-DeGroot-Marschak mechanism (BDM), and unincentivized introspection. Despite the theoretical advantages of incentive-compatible methods (BSR and BDM), we find that they impose significantly higher cognitive costs on participants, requiring more time and effort to implement, without delivering clear improvements in belief accuracy. In fact, BSR systematically leads to greater errors in reported beliefs compared to introspection, while BDM also reduces accuracy, though to a lesser extent. Surprisingly, individual differences in probabilistic reasoning skills do not mitigate these errors for BSR but do help improve accuracy under BDM. Our findings suggest that simpler, unincentivized approaches may offer comparable or even superior accuracy at a lower cognitive cost. These results have broad implications for the design of experiments and the interpretation of belief data in behavioral and experimental economics. |
| Keywords: | belief elicitation; induced beliefs; incentives; online experiment; |
| JEL: | C81 C89 D83 D91 |
| Date: | 2026–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:562 |
| By: | Clark, Jeremy |
| Abstract: | Repeated one-shot public good experiments commonly tell participants only oftheir group's total contribution after each round. In contrast, private charities sometimes publicise large contributions or contributors to encourage others to give or to bring recognition to donors. The effect of supplying such selective information on contribution levels is tested here experimentally. Following a control treatment with standard information, a second treatment also informs subjects of the maximum contribution made in their group after each round. In a third treatment, subjects are further given the opportunity to make costly rewards to the (unidentified) maximum contributor. Revealing generous contributions appears to raise average contributions slightly. Adding the ability to reward large contributors does not generate further increases, but raises the variance of contributions. |
| Keywords: | Public Economics |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263797 |
| By: | Jones, Rachel |
| Keywords: | International Development |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360995 |
| By: | Ahmed, Ayesha |
| Keywords: | Research Methods/Statistical Methods |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360823 |
| By: | Cristian Gil Sánchez (Acción Pública); Allison Benson (Acción Publica); Natalia Perez (Acción Publica) |
| Abstract: | This study examines whether cooperative perceptions, preferences, skills, and behaviors can be shaped through structured, game-based interventions. Using a lab-in-the-field experiment centered on a cooperative card game, we tested whether game play, paired with reflective learning, can foster both the motivation and the ability to cooperate. We find that while belief change was limited by ceiling effects among participants with strong baseline prosocial views, the intervention significantly increased preferences for cooperation, improved cooperative skills, and led to more cooperative behavior, particularly when a game experience is paired with reflective learning. We also observe variation in treatment effects by socioeconomic and demographic characteristics, with impacts being stronger among participants with higher education and income levels, and among those already concerned with inequality and climate change (examples of cooperative social challenges).Our findings highlight the relevance of understanding cooperation as a learnable practice, and points to the importance of combining both action and reflection in the design of cooperation-building tools. |
| Keywords: | Cooperation, perceptions, game-based learning, social experiments, Colombia |
| JEL: | D63 D91 I31 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:col:000089:022145 |
| By: | Shota Yasui; Tatsushi Oka; Undral Byambadalai; Yuki Oishi |
| Abstract: | We examine the impact of top-of-screen promotions on viewing time at ABEMA, a leading video streaming platform in Japan. To this end, we conduct a large-scale randomized controlled trial. Given the non-standard distribution of user viewing times, we estimate distributional treatment effects. Our estimation results document that spotlighting content through these promotions effectively boosts user engagement across diverse content types. Notably, promoting short content proves most effective in that it not only retains users but also motivates them to watch subsequent episodes. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11185 |
| By: | Nico Mutzner; Taha Yasseri; Heiko Rauhut |
| Abstract: | The introduction of artificial intelligence (AI) agents into human group settings raises essential questions about how these novel participants influence cooperative social norms. While previous studies on human-AI cooperation have primarily focused on dyadic interactions, little is known about how integrating AI agents affects the emergence and maintenance of cooperative norms in small groups. This study addresses this gap through an online experiment using a repeated four-player Public Goods Game (PGG). Each group consisted of three human participants and one bot, which was framed either as human or AI and followed one of three predefined decision strategies: unconditional cooperation, conditional cooperation, or free-riding. In our sample of 236 participants, we found that reciprocal group dynamics and behavioural inertia primarily drove cooperation. These normative mechanisms operated identically across conditions, resulting in cooperation levels that did not differ significantly between human and AI labels. Furthermore, we found no evidence of differences in norm persistence in a follow-up Prisoner's Dilemma, or in participants' normative perceptions. Participants' behaviour followed the same normative logic across human and AI conditions, indicating that cooperation depended on group behaviour rather than partner identity. This supports a pattern of normative equivalence, in which the mechanisms that sustain cooperation function similarly in mixed human-AI and all human groups. These findings suggest that cooperative norms are flexible enough to extend to artificial agents, blurring the boundary between humans and AI in collective decision-making. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.20487 |
| By: | Mayada Oudah; John Wooders |
| Abstract: | Facial expressions are central to human interaction, yet their role in strategic decision-making has received limited attention. We investigate how real-time facial communication influences cooperation in repeated social dilemmas. In a laboratory experiment, participants play a repeated Prisoner's Dilemma game under two conditions: in one, they observe their counterpart's facial expressions via gender-neutral avatars, and in the other no facial cues are available. Using state-of-the-art biometric technology to capture and display emotions in real-time, we find that facial communication significantly increases overall cooperation and, notably, promotes cooperation following defection. This restorative effect suggests that facial expressions help participants interpret defections less harshly, fostering forgiveness and the resumption of cooperation. While past actions remain the strongest predictor of behavior, our findings highlight the communicative power of facial expressions in shaping strategic outcomes. These results offer practical insights for designing emotionally responsive virtual agents and digital platforms that sustain cooperation in the absence of physical presence. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.15211 |
| By: | De Marchi, Elisa |
| Keywords: | Institutional and Behavioral Economics |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360713 |
| By: | Tontrup, Stephan; Sprigman, Christopher Jon |
| Abstract: | Our study examines how individuals perceive the moral agency of artificial intelligence (AI), and, specifically, whether individuals believe that by involving AI as their agent, they can offload to the AI some of their responsibility for a morally sensitive decision. Existing literature shows that people often delegate self-interested decisions to human agents to mitigate their moral responsibility for unethical outcomes. This research explores whether individuals will similarly delegate such decisions to AI to reduce moral costs. Our study shows that many individuals perceive the AI as capable of assuming moral responsibility. These individuals delegate to the AI and delegating leads them to act more assertively in their self-interest while experiencing lower moral costs. Participants (hereinafter, "Allocators") took part in a dictator game, allocating a $10 endowment between themselves and a Recipient. In the experimental treatment, Allocators could involve ChatGPT in their allocation decision, at the cost of incurring added time to complete the experiment. When engaged, the AI executed the transfer by informing the Recipient of a necessary payment code. Around 35% of Allocators chose to involve the AI, despite the opportunity costs of a much-prolonged process. To isolate the effect of the AI's perceived responsibility, a control condition replaced the AI with a non-agentive computer program, while maintaining identical decision protocols. This design controlled for factors such as social distance and substantive influence by the AI. Allocators who involved the AI transferred significantly less money to the Recipient, suggesting that delegating the transfer to AI reduced the moral costs associated with self-interested decisions. This is supported by the fact that prosocial individuals, who face higher moral costs from violating a norm and thus would without delegation transfer more than proself individuals, were significantly more likely to involve the AI. A responsibility measure indicates that Allocators who attributed more responsibility for the transfer to the AI were also more likely to involve the AI. The study suggests that AI systems provide human actors with an easily accessible, low-cost, and hard-to-monitor means of offloading personal moral responsibility, highlighting the need to consider in AI regulation not only the inherent risks of AI output, but also how AI's perceived moral agency can influence human behavior and ethical accountability in human-AI interaction. |
| Keywords: | AI, Delegation, Moral Outsourcing, Prosociality |
| JEL: | C91 D91 O33 D63 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335206 |
| By: | Kornhauser, Lewis; Lu, Yijia; Tontrup, Stephan |
| Abstract: | In this study, we suggest that crowding-out effects are unlikely when incentivizing behaviors that we refer to as mixed-motive-that is, behaviors motivated by both self-interest and prosociality. Vaccination is the prominent example we analyze: people vaccinate both to protect their own health and to contribute to herd immunity by protecting others. Building on signaling theory, we assume that people derive utility from signaling their prosociality. Incentives can crowd out prosocial motivation when they block the opportunity to send a clear prosocial signal, as in purely prosocial behaviors like charitable giving. Mixed-motive behaviors differ: they never allow for a clean prosocial signal in the first place, because self-interest is always a plausible motive. As such, providing financial incentives does not further constrain signaling opportunities, and we therefore predicted no crowding-out effects to emerge when incentivizing mixed-motive behaviors. Experimental evidence supports this prediction, and the pattern aligns with field studies suggesting that incentives may not crowd out vaccination uptake. |
| Keywords: | Crowding-Out, Incentives, Mixed Motives Behavior |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335207 |
| By: | Fabien Giauque; Mehdi Farsi |
| Abstract: | Dynamic social norms have been recognized as a promising approach to promote energy sufficiency. By highlighting trends and future shifts rather than current states, dynamic norms allow for a better focus on emerging norms that are not widely adopted. While existing studies predominantly examine behavioral outcomes, the underlying processes and trade-offs remain to be explored. This paper uses a discrete choice experiment (DCE) combined with a randomized controlled trial to study electricity saving preferences under various dynamic norms. An emphasis is placed on the rationale for the norm changes. The results show that dynamic norms framed in terms of growing concerns about energy supply security positively affect electricity saving goal, whereas those framed around climate change do not. The heterogeneity analyses suggest that dynamic norms shape behavior through two complementary mechanisms: they generate new preferences while simultaneously reinforcing existing ones. The concluding analysis identifies four distinct groups that vary systematically in their preferences for electricity sufficiency. |
| Keywords: | Electricity saving; Dynamic Norms; Energy supply security; Climate change; Discrete choice experiment; Latent Class Model; Mixed Logit Model; Value-Belief-Norm Theory |
| JEL: | D12 D91 Q48 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:irn:wpaper:25-09 |
| By: | Pawe{\l} Niszczota; Elia Antoniou |
| Abstract: | While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We use behavior in two canonical economic games to study whether people have different expectations when decisions are made by LLMs acting on their behalf instead of themselves. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they monitor the fairness of splits of resources (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2, 658) show three key findings. First, people find that offers from machines - when no acceptance is necessary - are judged to be less appropriate than when they come from humans, although there is no shift in the modal response. Second - when acceptance is necessary - it is more appropriate for a person to reject offers from machines than from humans. Third, receiving a rejection of an offer from a machine is no less socially appropriate than receiving the same rejection from a human. Overall, these results suggest that people apply different norms for machines deciding on how to split resources but are not opposed to machines enforcing the norms. The findings are consistent with offers made by machines now being viewed as having both a cognitive and emotional component. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.15312 |
| By: | Florence Bernays (University of Zurich); Marco Henriques Pereira (University of Zurich); Jochen Menges (University of Zurich) |
| Abstract: | This research examines how the emotional tone of human-AI interactions shapes ChatGPT and human behavior. In a between-subject experiment, we asked participants to express a specific emotion while working with ChatGPT (GPT-4.0) on two tasks, including writing a public response and addressing an ethical dilemma. We found that compared to interactions where participants maintained a neutral tone, ChatGPT showed greater improvement in its answers when participants praised ChatGPT for its responses. Expressing anger towards ChatGPT also led to a higher albeit smaller improvement relative to the neutral condition, whereas blaming ChatGPT did not improve its answers. When addressing an ethical dilemma, ChatGPT prioritized corporate interests less when participants expressed anger towards it, while blaming increases its emphasis on protecting the public interest. Additionally, we found that people used more negative, hostile, and disappointing expressions in human-human communication after interactions during which participants blamed rather than praised for their responses. Together, our findings demonstrate that the emotional tone people apply in human-AI interactions not only shape ChatGPT's outputs but also carry over into subsequent human-human communication. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.05104 |
| By: | Marisol Rodríguez Chatruc (Inter-American Development Bank, Montevideo, Uruguay); Ernesto Stein (School of Government and Public Transformation, Tecnológico de Monterrey); Razvan Vlaicu (Inter-American Development Bank, Washington, D.C., United States of America); Zuluaga, Victor (Banco de México, Mexico City, Mexico) |
| Abstract: | International trade increases aggregate welfare but also creates winners and losers, making it politically contentious. Recent research has established that individuals are more sensitive to anti-trade information about the prospect of employment loss than to pro-trade information about lower prices or greater variety. In this paper, we study how individual attitudes and beliefs change in response to information about employment losses (in import-competing sectors), gains (in export-oriented sectors), and the possibility of compensation for displaced workers. To this end, we conducted a large-scale survey experiment in 18 Latin American countries using nationally representative samples. We find that anti-trade information reduces support for trade even whencompensation is mentioned, while pro-trade messages increase support only when they emphasize job gains. Belief updating about trade’s employment effects seems to be a relevant mechanism. Our findings have important implications on what types of messaging work to increase support for trade: Although compensation is often recommended to build support for trade liberalizations, it can backfire in practice. At the same time, emphasizing employment creation in export sectors offers a more effective strategy to bolster public support for trade policies. |
| Keywords: | International trade, attitudes, employment, survey experiment, Latin America |
| JEL: | F13 D72 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:gnt:wpaper:21 |
| By: | Eldar Dadon (BGU); Marie Claire Villeval (Université Lumière Lyon 2, Université Jean- Monnet Saint-Etienne, emlyon business school, GATE, 69007, Lyon, France); Ro’i Zultan (BGU) |
| Keywords: | CSR, signaling, labor market, experiment |
| JEL: | C91 D83 J33 M5 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bgu:wpaper:2515 |
| By: | Clark, Jeremy |
| Abstract: | Are contributions involuntary public good experiments inflated because subjects are given money for their initial endowments? There is evidence that people receiving small, one time "windfall gains" have a high marginal propensity to consume them, and when doing so, exhibit greater riskseeking behaviour. Similar effects may be present in voluntary contribution experiments, causing subjects to contribute more to public goods than they would if using their own money. The effect ofwindfall money is tested by comparing VCM contribution rates when subjects supply their own endowments with those when endowments are provided, while holding constant the distribution of total promised earnings. |
| Keywords: | Consumer/Household Economics, Public Economics |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263796 |
| By: | Livia Alfonsi; Michal Bauer; Julie Chytilová; Edward Miguel |
| Abstract: | This paper investigates whether economic hardship undermines preferences for honesty. We use controlled, high-stake measures of cheating for private benefit in a large sample of 5, 664 Kenyans, exploiting three complementary sources of variation: experimentally manipulated monetary incentives to cheat, a randomized increase in the salience of one’s own financial situation, and the Covid‐19 income shock (exploiting randomized survey timing, with respondents interviewed before vs. during the crisis). We find that cheating behavior is highly responsive to financial incentives in the experiment. Covid-19 economic hardship—marked by a 51% drop in monthly earnings—leads to a sharp increase in the prevalence of cheating, and the effect increases gradually with prolonged hardship. The effects are largest among the most economically impacted and are amplified when the salience of one’s own financial situation is experimentally increased. The results demonstrate that while most individuals exhibit a strong preference against cheating under normal conditions (in line with the existing body of work), economic forces can account for a substantial share of variation in dishonesty: the estimated cheating rate rises from 29% under low stakes in normal times to 86% under high stakes during the crisis. |
| JEL: | C93 D91 O12 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34695 |
| By: | Wen Lou; Adri\'an A. D\'iaz-Faes; Jiangen He; Zhihao Liu; Vincent Larivi\`ere |
| Abstract: | Clinical trials shape medical evidence and determine who gains access to experimental therapies. Whether participation in these trials reflects the global burden of disease remains unclear. Here we analyze participation inequality across more than 62, 000 randomized controlled trials spanning 16 major disease categories from 2000 to 2024. Linking 36.8 million trial participants to country-level disease burden, we show that global inequality in clinical trial participation is overwhelmingly structured by country rather than disease. Country-level factors explain over 90% of variation in participation, whereas disease-specific effects contribute only marginally. Removing entire disease categories, including those traditionally considered underfunded, has little effect on overall inequality. Instead, participation is highly concentrated geographically, with a small group of countries enrolling a disproportionate share of participants across nearly all diseases. These patterns have persisted despite decades of disease-targeted funding and increasing alignment between research attention and disease burden within diseases. Our findings indicate that disease-vertical strategies alone cannot correct participation inequality. Reducing global inequities in clinical research requires horizontal investments in research capacity, health infrastructure, and governance that operate across disease domains. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04660 |
| By: | Tyack, Nicholas; Arouna, Aminou; Dembélé, Urbain; Goeschl, Timo |
| Abstract: | We study how confidence bias affects investment in learning via experimentation, a mechanism critical for technology adoption under uncertainty. We hypothesize that bias direction and strength predict how willingness to experiment diverges from unbiased agents. We measure revealed and stated demand for experimenting with drought-resistant crop varieties of 1, 957 farmers in West Africa, a climate change hotspot. Consistent with our hypothesis, confidence bias strongly predicts willingness to experiment. The effect, however, is driven exclusively by underconfident agents, among whom females are overrepresented. In deteriorating environments, this behavioral friction undercuts effective technology diffusion and risks trapping individuals in maladapted production environments. |
| Keywords: | Underconfidence; overconfidence; experimentation; adaptation |
| Date: | 2026–01–23 |
| URL: | https://d.repec.org/n?u=RePEc:awi:wpaper:0769 |
| By: | Jahani, Eaman; Kolic, Blas; Tonneau, Manuel; Lin, Hause (Massachusetts Institute of Technology); Barkoczi, Daniel; Fraiberger, Samuel P. |
| Abstract: | Online hate spreads rapidly, yet little is known about whether preventive and scalable strategies can curb it. We conducted the largest randomized controlled trial of hate speech prevention to date: a 20-week messaging campaign on X in Nigeria targeting ethnic hate. 73, 136 users who had previously engaged with hate speech were randomly assigned to receive prosocial video messages from Nigerian celebrities. The campaign reduced hate content by 2.5% to 5.5% during treatment, with about 75% of the reduction persisting over the following four months. Reaching a larger share of a user’s audience reduced amplification of that user’s hate posts among both treated and untreated users, cutting hate reposts by over 50% for the most exposed accounts. Scalable messaging can limit online hate without removing content. |
| Date: | 2026–01–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:qmvuh_v1 |
| By: | Romain Baeriswyl; Kene Boun My; Camille Cornand |
| Abstract: | In a monetary system in which risk-free and risky money coexist, Gresham's law predicts that people will prefer to hoard risk-free money as a store of value and spend risky money as a medium of exchange. Establishing a payment system on the basis of risk-free money, such as a retail CBDC, while maintaining the fractional reserve banking system in place poses numerous challenges. In a laboratory experiment, we demonstrate that when the holding of risk-free money is unrestricted, people hold and pay with it extensively. However, when the ability to hold risk-free money is limited by a ceiling or an unattractive interest rate, people tend to hoard risk-free money and use risky money for payments. |
| Keywords: | Central Bank Digital Currency, Gresham's law, Laboratory experiment |
| JEL: | E52 E58 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2026-03 |
| By: | Tontrup, Stephan; Arlen, Jennifer; Sprigman, Christopher Jon |
| Abstract: | People often act prosocially and voluntarily conform to social and legal norms. This has fueled the idea that law can guide behavior through its expressive power. By contrast, we offer a theoretical and experimental framework suggesting that people strategically alter their decision-making environment to shift the norm applicable to their actions to one that is in their self-interest and to the detriment of others. Norm-shifting is one strategy within a broader concept we refer to as Behavioral Self-Management (BSM). To test norm-shifting, we implement a dictator game in which Allocators are offered an effort task before allocating a sum between themselves and a Recipient. Allocators receive the same endowment whether or not they work. We hypothesize that many will undertake the task to shift the applicable fairness norm from equal division to an effort-based norm that justifies their retaining a larger share. Prior evidence shows that costly effort is widely perceived as legitimizing unequal outcomes. We find that many Allocators decide to work, thereby reducing average transfers. Their work choices are strategic: their odds of working are higher the more they expect work to shift the fairness norm in their favor and the more prosocial they are-that is, the higher the moral costs they face for violating the fairness norm. Finally, Allocators who work make transfers that they expect to conform to an effort-based norm in the view of others, to maintain their self- and social-image. Our findings have implications for compliance with the law and with social norms. BSM can enable selfish non-compliance by undermining the social norms that underpin the law or by establishing social norms that provide justification for violation, while avoiding the social disapproval that would otherwise result. |
| Keywords: | Behavioral Self-management, Norm-shifting, Work, Self-and Social Image |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335552 |
| By: | Fountain, John |
| Abstract: | Inferences derived from Starmer's (1992) experimental evidence concerning Expected Utility (EUT), Fanning Out (FO), and Fanning In (Fl) theories are both incomplete and incorrect A subjectivist Bayesian approach based on calculating posterior probability distributions for experimental outcomes is used to quantify the degree of support for each theory and to make coherent inferences about the relative performance of FO and H theories in explaining violations of EUT. |
| Keywords: | Research Methods/Statistical Methods |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263711 |
| By: | Tsuyoshi Nihonsugi (Department of Economics, Osaka University of Economics); Yoshio Kamijo (Faculty of Political Science and Economics, Waseda University); Satoshi Taguchi (Graduate School of Commerce, Doshisha University); Shigeharu Okajima (Graduate School of International Cooperation Studies, Kobe University); Hiroko Okajima (Graduate School of Economics, Nagoya University) |
| Abstract: | This study examines how gender quotas influence job application decisions and occupational choices in Japan, and how these effects vary across individual characteristics. Using a choice-based conjoint experiment with 1, 167 participants, we analyze preferences for positions with and without gender quotas across different job types. We find that gender quotas significantly increase women's application likelihood by approximately 10 percentage points, with the strongest effects among high-performing employed women, while not discouraging applications from comparably qualified men. Beyond increasing female representation, quotas enable women to make occupational choices that better align with their preferences and are associated with higher expected productivity and workplace well-being. Further analysis reveals that support for gender quotas relates systematically to personality traits, gender role beliefs, and prior experiences—notably, men who recognize past gender advantages show greater support for quotas. These findings provide actionable insights for designing inclusive recruitment strategies and diversity policies in non-Western contexts, demonstrating that well-designed quotas can promote both equity and efficiency in labor markets. |
| Keywords: | gender quotas, affirmative action, gender gap, hiring discrimination, occupational segregation, labor market |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:wap:wpaper:2529 |
| By: | Fountain, John; McCosker, Michael |
| Abstract: | An experiment and subjective Bayesian statistical methods are used to investigate how robust the common consequence effect is to changes in frame that make pure increases in risk transparent. We find that subjects avoid pure increases in risk when such risks are transparent, but not otherwise, and that there is no correlation between risk attitudes in frames that alternately mask and make transparent pure increases in risk. The common consequence effect is nearly frame independent, but no more predictable (marginally or jointly) than by chance in the sense of Laplace's law of succession. |
| Keywords: | Research Methods/Statistical Methods, Risk and Uncertainty |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263716 |
| By: | Fountain, John; McCosker, Michael; Morris, Dean |
| Abstract: | An experiment and operational subjective Bayesian statistical methods are used to investigate the relation between risk attitudes in the loss domain and framing effects. We find that subjects avoid pure increases in risk when such risks are transparent, that there is little or no correlation between risk attitudes in frames that alternately mask and make transparent pure increases in risk, and that analysing risk attitudes when prospects are presented as lists of prizes and probabilites overstates the likelihood of risk seeking in the loss domain. In general GEUT fails to predict better than a naive theory holding a uniform prior and Bayesian updating. The one exception is in a frame (viewed marginally) where costs of acquiring and processing information are low. |
| Keywords: | Risk and Uncertainty |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263764 |
| By: | Fabian Stephany; Ole Teutloff; Angelo Leone |
| Abstract: | The growing adoption of artificial intelligence (AI) technologies has heightened interest in the labour market value of AI-related skills, yet causal evidence on their role in hiring decisions remains scarce. This study examines whether AI skills serve as a positive hiring signal and whether they can offset conventional disadvantages such as older age or lower formal education. We conduct an experimental survey with 1, 700 recruiters from the United Kingdom and the United States. Using a paired conjoint design, recruiters evaluated hypothetical candidates represented by synthetically designed resumes. Across three occupations - graphic designer, office assistant, and software engineer - AI skills significantly increase interview invitation probabilities by approximately 8 to 15 percentage points. AI skills also partially or fully offset disadvantages related to age and lower education, with effects strongest for office assistants, where formal AI certification plays an additional compensatory role. Effects are weaker for graphic designers, consistent with more skeptical recruiter attitudes toward AI in creative work. Finally, recruiters' own background and AI usage significantly moderate these effects. Overall, the findings demonstrate that AI skills function as a powerful hiring signal and can mitigate traditional labour market disadvantages, with implications for workers' skill acquisition strategies and firms' recruitment practices. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13286 |
| By: | Carina I. Hausladen; Marcel H. Schubert; Christoph Engel |
| Abstract: | Behavior in repeated public goods games continues to challenge standard theory: heterogeneous social preferences can explain first-round contributions, but not the substantial volatility observed across repeated interactions. Using 50, 390 decisions from 2, 938 participants, we introduce two methodological advances to address this gap. First, we cluster behavioral trajectories by their temporal shape using Dynamic Time Warping, yielding distinct and theoretically interpretable behavioral types. Second, we apply a hierarchical inverse Q-learning framework that models decisions as discrete switches between latent cooperative and defective intentions. This approach reveals a large (21.4%) and previously unmodeled behavioral type -- Switchers -- who frequently reverse intentions rather than commit to stable strategies. At the same time, the framework recovers canonical strategic behaviors such as persistent cooperation and free-riding. Substantively, recognizing intentional volatility helps sustain cooperation: brief defections by Switchers often reverse, so strategic patience can prevent unnecessary breakdowns. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08803 |
| By: | Vadim Grishchenko (Bank of Russia, Higher School of Economics, Russian Federation); Maria Lymar (Bank of Russia, MSU, Russian Federation); Andrei Sinyakov (Bank of Russia, Russian Federation) |
| Abstract: | In theory, the anchoring of household inflation expectations contributes a lot to the success of inflation targeting, since inflation expectations may significantly influence consumer and financial decisions. In this paper, we estimate the causal relationship between information and the inflation expectations of Russian households using a randomized controlled trial (RCT) approach applied to the data of the 6th wave of the Survey of Consumer Finance (2024). To the best of our knowledge, this is the first study of this kind based on Russian data. According to our estimates, direct, quantitative estimates of future inflation are more sensitive to incoming information. Respondents react most strongly to the treatment about growth in the money supply in the previous year, adjusting their inflation expectations upwards. At the same time, as opposed to research based on data from other countries, we find no relationship between information about inflation in the past year or about the central bank's target and its success in inflation targeting, on the one hand, and household inflation expectations, on the other. This means that monetary policy should react more strongly to pro-inflationary shocks to achieve the target. Actions, not words, matter the most. |
| Keywords: | inflation expectations, randomized controlled trial (RCT), Household Survey of Consumer Finances, central bank communication policy |
| JEL: | C83 C93 D84 E31 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:bkr:wpaper:wps148 |
| By: | Thomas H. Costello; Kellin Pelrine; Matthew Kowal; Antonio A. Arechar; Jean-Fran\c{c}ois Godbout; Adam Gleave; David Rand; Gordon Pennycook |
| Abstract: | Large language models (LLMs) have been shown to be persuasive across a variety of context. But it remains unclear whether this persuasive power advantages truth over falsehood, or if LLMs can promote misbeliefs just as easily as refuting them. Here, we investigate this question across three pre-registered experiments in which participants (N = 2, 724 Americans) discussed a conspiracy theory they were uncertain about with GPT-4o, and the model was instructed to either argue against ("debunking") or for ("bunking") that conspiracy. When using a "jailbroken" GPT-4o variant with guardrails removed, the AI was as effective at increasing conspiracy belief as decreasing it. Concerningly, the bunking AI was rated more positively, and increased trust in AI, more than the debunking AI. Surprisingly, we found that using standard GPT-4o produced very similar effects, such that the guardrails imposed by OpenAI did little to revent the LLM from promoting conspiracy beliefs. Encouragingly, however, a corrective conversation reversed these newly induced conspiracy beliefs, and simply prompting GPT-4o to only use accurate information dramatically reduced its ability to increase conspiracy beliefs. Our findings demonstrate that LLMs possess potent abilities to promote both truth and falsehood, but that potential solutions may exist to help mitigate this risk. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.05050 |
| By: | Bossaerts, P.; Ioannidis, K.; Woods, R.; Yadav, N. |
| Abstract: | We study market equilibrium in settings with indivisible goods and tight budget constraints, where a traditional Walrasian Equilibrium (WE) may fail to exist. We introduce the Complexity Compensating Equilibrium (CCE), in which prices endogenously render the budget problem computationally difficult. Complexity induces heterogeneous demands even among agents with homogeneous preferences, as individuals allocate varying levels of cognitive effort. We define the equilibrium region as the set of price configurations that satisfy the necessary economic and computational conditions for equilibrium to exist. In this region, price configurations maximize the difficulty of the budget problem in addition to satisfying market clearing conditions. We evaluate the predictions of CCE through a controlled market experiment. We find that trading prices consistently force the budget problem to the equilibrium region. Further supporting and central to the CCE framework, the equilibrium bundles of goods generate markedly different utility levels across agents. This outcome contradicts a core feature of WE, namely, the equalization of utilities. In a setting where it exists, we reject WE on both prices and utilities, in favour of CCE. |
| Keywords: | General Equilibrium, Indivisibilities, Cognitive Effort, NP hard, Complexity Compensating Equilibrium, Walrasian Equilibrium, Markets Experiment |
| JEL: | D51 C92 C62 |
| Date: | 2026–01–02 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2603 |
| By: | Kerr, H. W. T.; Hebblethwaite, P. D.; Holloway, K. N. |
| Keywords: | Crop Production/Industries, Production Economics |
| URL: | https://d.repec.org/n?u=RePEc:ags:notarc:266267 |
| By: | Giles, David; Lieberman, Offer |
| Abstract: | We consider the common situation in which the application of the Durbin-Watson test for serial correlation in the errors of a regression model is preceded by a preliminary t-test for the significance of one of the coefficients. The effect of such pre-testing on the size and power of the Durbin-Watson test is examined in a Monte Carlo experiment. |
| Keywords: | Financial Economics |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263004 |
| By: | Apoorva Lal; Guido Imbens; Peter Hull |
| Abstract: | We propose a method for estimating long-term treatment effects with many short-term proxy outcomes: a central challenge when experimenting on digital platforms. We formalize this challenge as a latent variable problem where observed proxies are noisy measures of a low-dimensional set of unobserved surrogates that mediate treatment effects. Through theoretical analysis and simulations, we demonstrate that regularized regression methods substantially outperform naive proxy selection. We show in particular that the bias of Ridge regression decreases as more proxies are added, with closed-form expressions for the bias-variance tradeoff. We illustrate our method with an empirical application to the California GAIN experiment. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.06359 |
| By: | Bruno Ferman; Davi Siqueira; Vitor Possebom |
| Abstract: | This paper develops a unified framework for partial identification and inference in stratified experiments with attrition, accommodating both equal and heterogeneous treatment shares across strata. For equal-share designs, we apply recent theory for finely stratified experiments to Lee bounds, yielding closed-form, design-consistent variance estimators and properly sized confidence intervals. Simulations show that the conventional formula can overstate uncertainty, while our approach delivers tighter intervals. When treatment shares differ across strata, we propose a new strategy, which combines inverse probability weighting and global trimming to construct valid bounds even when strata are small or unbalanced. We establish identification, introduce a moment estimator, and extend existing inference results to stratified designs with heterogeneous shares, covering a broad class of moment-based estimators which includes the one we formulate. We also generalize our results to designs in which strata are defined solely by observed labels. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.12566 |