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on Experimental Economics |
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Issue of 2023‒08‒14
twenty-two papers chosen by |
| By: | Alfitian, Jakob (University of Cologne); Deversi, Marvin (Education Y); Sliwka, Dirk (University of Cologne) |
| Abstract: | We present a natural field experiment on promoting pay equity through simple modifications to the salary review process involving 623 middle managers and 8, 951 subordinate employees of a large technology firm. We first document a gender gap not only in salary levels but also in salary increases. Our treatments provide for a gender-blind reallocation of the salary increase budget available to middle managers aimed at promoting pay equity, along with different variants of a corresponding decision guidance. We show that the budget reallocation combined with an explicit decision guidance, while still leaving middle managers discretion in allocating the budget, can completely eliminate the gender gap in salary increases. The treatments also do not appear to undermine the desired performance differentiation in salary increases. We thus show that simple modifications to the salary review process can go a long way toward achieving pay equity by preventing gender gaps from widening throughout employees' careers. |
| Keywords: | gender pay gap, pay equity, randomized controlled trial, salary structure |
| JEL: | J31 J71 M52 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16278&r=exp |
| By: | Francesco Agostinelli; Ciro Avitabile; Matteo Bobba |
| Abstract: | This paper provides novel insights into the science of scaling by examining an educational mentoring program in Mexico. Our analysis encompasses two separate field experiments, and takes advantage of a unique opportunity to learn from the government's implementation of the program on a large scale. While the originally implemented program at scale demonstrates limited effectiveness, the introduction of a new modality with enhanced mentor training significantly improves children's outcomes. This improvement is observed in both the field experiment and the subsequent large-scale government adoption. We also find that the new program's enhanced mentor-parent interactions stimulate parental engagement at the community-school level, which emerges as a critical factor in facilitating the program's scalability. |
| JEL: | C90 C93 D02 I3 J1 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31407&r=exp |
| By: | 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); 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) |
| Abstract: | We analyze whether exposure to a nature documentary increases pro-environmental behavior (PEB). We test this causal link in an experiment where subjects viewed a video featuring either an urban (control treatment) or a nature setting (nature treatment). We consider two types of behavior: a monetary donation to an environmental non-governmental organization (ENGO) that we call an eco-donation, and subsequently, a non-monetary decision (i.e., recycle or not recycle headphone protectors) that we call an eco-action. We find that virtual exposure to nature boosts both eco-donation and eco-action. Interestingly, the increase in PEB only occurs for individuals who express low environmental values. We did not find any negative or positive spillover effects on the eco-action. We finally provide robustness checks and discuss policy implications. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03847453&r=exp |
| By: | Leib, Margarita (Tilburg University); Köbis, Nils (Max Planck Institute for Human Development); Rilke, Rainer Michael (WHU Vallendar); Hagens, Marloes (Erasmus University Rotterdam); Irlenbusch, Bernd (University of Cologne) |
| Abstract: | Artificial Intelligence (AI) increasingly becomes an indispensable advisor. New ethical concerns arise if AI persuades people to behave dishonestly. In an experiment, we study how AI advice (generated by a Natural-Language-processing algorithm) affects (dis)honesty, compare it to equivalent human advice, and test whether transparency about advice source matters. We find that dishonesty-promoting advice increases dishonesty, whereas honesty-promoting advice does not increase honesty. This is the case for both AI and human advice. Algorithmic transparency, a commonly proposed policy to mitigate AI risks, does not affect behaviour. The findings mark the first steps towards managing AI advice responsibly. |
| Keywords: | Artificial Intelligence, machine behaviour, behavioural ethics, advice |
| JEL: | C91 D90 D91 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16293&r=exp |
| By: | Luca Henkel |
| Abstract: | Observed individual behavior in the presence of ambiguity shows insufficient responsiveness to changes in subjective likelihoods. Despite being integral to theoretical models and relevant in many domains, evidence on the causes and determining factors of such likelihood insensitive behavior is scarce. This paper investigates the role of beliefs in the form of ambiguity perception – the extent to which a decision-maker has difficulties assigning a single probability to each possible event – as a potential determinant. Using an experiment, I elicit measures of ambiguity perception and likelihood insensitivity and exogenously vary the level of perceived ambiguity. The results provide strong support for a perception-based explanation of likelihood insensitivity. The two measures are highly correlated at the individual level, and exogenously increasing ambiguity perception increases insensitivity, suggesting a causal relationship. In contrast, ambiguity perception is unrelated to ambiguity aversion – the extent to which a decision-maker dislikes the presence of ambiguity. |
| Keywords: | Ambiguity, decision-making under uncertainty, likelihood insensitivity, multiple prior models |
| JEL: | D81 D83 D91 C91 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2023_440&r=exp |
| By: | Nicola Campigotto; Marco Catola; Simone D’Alessandro; Pietro Guarnieri; Lorenzo Spadoni |
| Abstract: | This paper explores the potential of voluntary consumption quotas as a strategy to address resource supply shortages. The results of an incentivized online experiment are presented in which a Nash demand game was used to model an energy consumption problem. Participants had the option to join an energy conservation programme by accepting a consumption quota. Those who accepted the quota traded off their maximum demand for energy in exchange for the certainty that their demand would be met, while those who rejected the quota could demand and possibly earn more but risked suffering from a power outage, in which case they received nothing. Three different quota schemes are examined, and their policy implications are discussed. Our findings suggest that voluntary quotas may lead to a significant decrease in overall demand and contribute to enhancing consumption security. |
| Keywords: | energy consumption, online experiment, Nash demand game, power outages, voluntary quotas |
| JEL: | C72 C99 Q48 |
| Date: | 2023–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:pie:dsedps:2023/299&r=exp |
| By: | Nikhil Agarwal; Alex Moehring; Pranav Rajpurkar; Tobias Salz |
| Abstract: | While Artificial Intelligence (AI) algorithms have achieved performance levels comparable to human experts on various predictive tasks, human experts can still access valuable contextual information not yet incorporated into AI predictions. Humans assisted by AI predictions could outperform both human-alone or AI-alone. We conduct an experiment with professional radiologists that varies the availability of AI assistance and contextual information to study the effectiveness of human-AI collaboration and to investigate how to optimize it. Our findings reveal that (i) providing AI predictions does not uniformly increase diagnostic quality, and (ii) providing contextual information does increase quality. Radiologists do not fully capitalize on the potential gains from AI assistance because of large deviations from the benchmark Bayesian model with correct belief updating. The observed errors in belief updating can be explained by radiologists’ partially underweighting the AI’s information relative to their own and not accounting for the correlation between their own information and AI predictions. In light of these biases, we design a collaborative system between radiologists and AI. Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI. |
| JEL: | C50 C90 D47 D83 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31422&r=exp |
| By: | Benjamin Ouvrard (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); Arnaud Reynaud (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - 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); Stéphane Cezera (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - 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); Alban Thomas (UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, US ODR - Observatoire des Programmes Communautaires de Développement Rural - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Dishant Jojit James (Wyzsza Szkola Bankowa University); Murudaiah Shivamurthy (Department of Agricultural Extension, GKVK, UAS, Bangalore) |
| Abstract: | We use a framed-field experiment to analyze the preferences of Indian farmers regarding water sharing. Farmers play a dictator game (DG) behind the veil of ignorance in which a limited quantity of water has to be allocated between two farmers. We vary the equity/efficiency trade-off by introducing some heterogeneity between farmers' productivity and by considering an upstream/downstream spatial configuration. We first show that generosity in the DG is high (on average, respectively 44% and 47% of the total quantity of water or the total profit are left by the dictator). Only a small proportion of farmers act in the DG as selfish profit maximizers, a majority of them adopting efficient, egalitarian in payoff or egalitarian in quantity behaviors. We then show that it is possible to induce more efficient water allocation behaviors in the DG by modifying farmer's choice architecture. A loss framing induces farmers to share more efficiently the water resource, but only when the most productive farmer is located downstream. On the contrary, we find mild evidence that farmers choose less often the efficient solution with a gain framing. |
| Keywords: | Dictator Game, Framed-field experiment, Framing, Water sharing |
| Date: | 2023–07–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-04150233&r=exp |
| By: | Abeler, Johannes (University of Oxford); Huffman, David B. (University of Pittsburgh); Raymond, Collin (Purdue University) |
| Abstract: | Using field and laboratory experiments, we demonstrate that the complexity of incentive schemes and worker bounded rationality can affect effort provision, by shrouding attributes of the incentives. In our setting, complexity leads workers to over-provide effort relative to a fully rational benchmark, and improves efficiency. We identify contract features, and facets of worker cognitive ability, that matter for shrouding. We find that even relatively small degrees of shrouding can cause large shifts in behavior. Our results illustrate important implications of complexity for designing and regulating workplace incentive contracts. |
| Keywords: | complexity, bounded rationality, shrouded attribute, ratchet effect, dynamic incentives, field experiments |
| JEL: | D8 D9 J2 J3 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16284&r=exp |
| By: | Spyros Galanis (Durham University); Christos A. Ioannou (Universite Paris 1 Pantheon - Sorbonne, Paris, France); Stelios Kotronis (Durham University) |
| Abstract: | This is a Supplementary appendix to “Information Aggregation Under Ambiguity: Theory and Experimental Evidence†|
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:dur:durham:2023_05&r=exp |
| By: | Spyros Galanis (Durham University); Christos A. Ioannou (Universite Paris 1 Pantheon - Sorbonne, Paris, France); Stelios Kotronis (Durham University) |
| Abstract: | We study information aggregation in a dynamic trading model. We show theoretically that separable securities, introduced by Ostrovsky (2012) in the context of Expected Utility, no longer aggregate information if some traders have imprecise beliefs and are ambiguity averse. Moreover, these securities are prone to manipulation as the degree of information aggregation can be influenced by the initial price set by the uninformed market maker. These observations are also confirmed in our laboratory experiment using prediction markets. We define a new class of strongly separable securities, which are robust to the above considerations, and show that they characterize information aggregation in both strategic and non-strategic environments. We derive several testable predictions, which we are able to confirm in the laboratory. Finally, we show theoretically that strongly separable securities are both sufficient and necessary for information aggregation but, strikingly, there does not exist a security that is strongly separable for all information structures |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:dur:durham:2023_04&r=exp |
| By: | Stefano Piasenti (HU Berlin); Marica Valente (University of Innsbruck); Roel van Veldhuizen (Lund University); Gregor Pfeifer (University of Sydney) |
| Abstract: | How do men and women differ in their persistence after experiencing failure in a competitive environment? We tackle this question by combining a large online experiment (N=2, 086) with machine learning. We find that when losing is unequivocally due to merit, both men and women exhibit a significant decrease in subsequent tournament entry. However, when the prior tournament is unfair, i.e., a loss is no longer necessarily based on merit, women are more discouraged than men. These results suggest that transparent meritocratic criteria may play a key role in preventing women from falling behind after experiencing a loss. |
| Keywords: | competitiveness; gender; fairness; machine learning; online experiment; |
| JEL: | C90 D91 J16 C14 |
| Date: | 2023–07–14 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:410&r=exp |
| By: | Marco Morucci; Vittorio Orlandi; Harsh Parikh; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky |
| Abstract: | Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework tests for violations of external validity and ignorability under milder assumptions. When only one assumption is violated, we provide semi-parametrically efficient treatment effect estimators. However, our no-free-lunch theorem highlights the necessity of accurately identifying the violated assumption for consistent treatment effect estimation. We demonstrate the applicability of our approach in three real-world case studies, highlighting its relevance for practical settings. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.01449&r=exp |
| By: | Kai Barron (WZB - Wissenschaftszentrum Berlin für Sozialforschung); Steffen Huck (UCL - University College of London [London], WZB - Wissenschaftszentrum Berlin für Sozialforschung); Philippe Jehiel (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 - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - 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 - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UCL - University College of London [London]) |
| Abstract: | This study explores selection neglect in an experimental investment game where individuals can learn from others' outcomes. Experiment 1 examines aggregate-level equilibrium behavior. We find strong evidence of selection neglect and corroborate several comparative static predictions of Jehiel's (2018) model, showing that the severity of the bias is aggravated by the sophistication of other individuals and moderated when information is more correlated across individuals. Experiment 2 focuses on individual decision-making, isolating the influence of beliefs from possible confounding factors. This allows us to classify individuals according to their degree of naivety and explore the limits of, and potential remedies for, selection neglect. |
| Keywords: | Selection neglect, beliefs, overoptimism, survivorship bias, experiment |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-04154345&r=exp |
| By: | Annalena Oppel |
| Abstract: | This paper seeks to understand whether the way in which inequality is communicated through measurements influences individuals' fairness perceptions regarding wealth inequality. It begins from the premise that prominent measures of inequality, such as the Gini coefficient, fall short of providing an intuitive understanding of inequality for most people. Following approaches in the behavioural economics domain, the paper explores the effects of four different presentations of inequality in a survey experiment. |
| Keywords: | Wealth inequality, Survey, Experiments, Wealth, Measurement, Inequality measurement |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2023-86&r=exp |
| By: | Wieser, Luisa; Abraham, Martin; Schnabel, Claus; Niessen, Cornelia; Wolff, Mauren |
| Abstract: | This paper examines supervisors' considerations about (not) using monitoring technologies to keep track of subordinates and their work performance. We conduct a factorial survey experiment. The hypothetical descriptions of workplace situations - so-called vignettes - create a situation where the surveyed supervisor is faced with a new team of subordinates and a given technology that can be used to track employees at work. Several components of the situation are randomly varied across vignettes and respondents. We find that supervisors are less interested in using monitoring technologies if the monitoring technology targets people rather than tasks and if the time effort for the supervisor is high. Supervisors' monitoring interest increases if their subordinates interact with sensitive firm data and the data evaluation is AI supported. Thus, our results confirm that supervisors take the costs and benefits of electronic performance monitoring into consideration regarding their attitude towards monitoring technologies at work. |
| Keywords: | employee performance monitoring, workplace technology, factorial survey experiment, Germany |
| JEL: | M50 D22 J01 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:faulre:127&r=exp |
| By: | Tatyana Avilova; Claudia Goldin |
| Abstract: | Economics is among the most popular undergraduate majors. However, even at the best research universities and liberal arts colleges men outnumber women by two to one, and overall there are about 2.5 males to every female economics major. The Undergraduate Women in Economics (UWE) Challenge was begun in 2015 for one year as a randomized controlled trial with 20 treatment and 68 control schools to evaluate the impact of light-touch interventions to recruit and retain female economics majors. Treatment schools received funding, guidance, and access to networking with other treatment schools to implement programs such as providing better information about the application of economics, exposing students to role models, and updating course content and pedagogy. Using 2001-2021 data from the Integrated Postsecondary Education Data System (IPEDS) on graduating BAs, we find that UWE was effective in increasing the fraction of female BAs who majored in economics relative to men in liberal arts colleges. Large universities did not show an impact of the treatment, although those that implemented their own RCTs showed moderate success in encouraging more women to major in economics. We speculate on the reasons for differential treatment impact. |
| JEL: | A22 C93 I21 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31432&r=exp |
| By: | Alberto Abadie; Anish Agarwal; Guido Imbens; Siwei Jia; James McQueen; Serguei Stepaniants |
| Abstract: | Business/policy decisions are often based on evidence from randomized experiments and observational studies. In this article we propose an empirical framework to estimate the value of evidence-based decision making (EBDM) and the return on the investment in statistical precision. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.13681&r=exp |
| By: | Daniel Krähmer (Ludwig-Maximilians-University, Munich) |
| Abstract: | Multifactorial designs are used to study the (joint) impact of two or more factors on an outcome. They typically occur in conjoint, choice, and factorial survey experiments but have recently gained increasing popularity in field experiments, too. Technically, they allow researchers to investigate moderation as an instance of treatment heterogeneity by crossing multiple treatments. Naturally, multifactorial designs quickly spawn a spiraling number of distinct treatment combinations: even a moderately complex design of two factors with three levels each yields 32 unique combinations. For more elaborate setups, full factorials can easily produce dozens of distinct combinations, rendering the visualization of results difficult. This presentation introduces the new Stata command mfcurve as a potential remedy. Mimicking the appearance of a specification curve, mfcurve produces a two-part chart: the graph’s upper panel displays average effects for all distinct treatment combinations; its lower panel indicates the presence or absence of any level given the respective treatment condition. Unlike existing visualization techniques, this enables researchers to plot and inspect results from multifactorial designs much more comprehensively. Highlighting potential applications, the presentation will demonstrate mfcurve’s most important features and options, which currently include replacing point estimates by box plots and testing results for statistical significance. |
| Date: | 2023–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:boc:dsug23:03&r=exp |
| By: | Stijn Baert; Jolien Herregods; Philippe Sterkens (-) |
| Abstract: | In this study, we present a state-of-the-art scenario experiment which, for the first time in the literature, directly measures the stigma surrounding job candidates with tattoos and piercings using real recruiters. We find that job candidates with body art are perceived as less pleasant to work with, less honest, less emotionally stable, less agreeable, less conscientious and less manageable. This goes hand in hand with lower hireability for men with body art but not for women. Compared to candidates who reveal obesity, a characteristic we also randomise, those with body art score better overall in terms of hireability and rated personality, similar in terms of rated taste to collaborate but worse in terms of rated direct productivity drivers. |
| Keywords: | body art, obesity, stigma, personality, hiring, taste discrimination, statistical discrimination |
| JEL: | C91 J24 J71 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:23/1072&r=exp |
| By: | Huffman, David B. (University of Pittsburgh); Raymond, Collin (Purdue University); Shvets, Julia (University of Cambridge) |
| Abstract: | A long-standing puzzle is how overconfidence can persist in settings characterized by repeated feedback. This paper studies managers who participate repeatedly in a high-powered tournament incentive system, learning relative performance each time. Using reduced form and structural methods we find that: (i) managers make overconfident predictions about future performance; (ii) managers have overly-positive memories of past performance; (iii) the two phenomena are linked at an individual level. Our results are consistent with models of motivated beliefs in which individuals are motivated to distort memories of feedback and preserve unrealistic expectations. |
| Keywords: | overconfidence, memory, tournament, motivated beliefs |
| JEL: | D82 D83 J33 L25 L81 M52 M54 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16283&r=exp |
| By: | Claudia, Pitterle |
| Abstract: | The aim of this literature review is to systematically summarize the existing knowledge and theories on the subject of decision-making behavior in general and in particular, when doctors have to decide for or against insurance for their own practice. Publications on decision psychology, behavioral economics, consumer behavior and modern brain research were evaluated. Special interest was paid to studies with regard to insurance demand and the regulatory framework. Each branch of science deals with decisions that people make consciously and unconsciously. Conducted worldwide studies of insurance demand have been directed to try to confirm or disprove certain theories using experiments. In summary, research in recent years has been increasingly in the area of behavioral economics in particular behavioral patterns. It has been confirmed that decision behavior related to insurance demand is very much shaped by determinants such as risk, uncertainty, and cognitive systems. Insurance consulting must continue to take these determinants into account in a more targeted manner in the future. |
| Keywords: | decision-making, doctors in private practice, insurance demand, behavioral patterns |
| JEL: | D83 D86 D91 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:117730&r=exp |