nep-exp New Economics Papers
on Experimental Economics
Issue of 2022‒09‒26
sixteen papers chosen by

  1. Closing the gender STEM gap - A large-scale randomized-controlled trial in elementary schools By Kerstin Grosch; Simone Haeckl; Martin G. Kocher
  2. Motives Behind Cooperation in Finitely Repeated Prisoner's Dilemma By Anujit Chakraborty
  3. Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias? By Brodeur, Abel; Cook, Nikolai M.; Hartley, Jonathan S.; Heyes, Anthony
  4. Material Incentives and Effort Choice: Evidence from an Online Experiment Across Countries By Elwyn Davies; Marcel Fafchamps
  5. We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments By Brodeur, Abel; Cook, Nikolai; Heyes, Anthony
  6. Not Learning from Others By John J. Conlon; Malavika Mani; Gautam Rao; Matthew W. Ridley; Frank Schilbach
  7. Unconditional Cash and Family Investments in Infants: Evidence from a Large-Scale Cash Transfer Experiment in the U.S. By Lisa A. Gennetian; Greg Duncan; Nathan A. Fox; Katherine Magnuson; Sarah Halpern-Meekin; Kimberly G. Noble; Hirokazu Yoshikawa
  8. Risk-taking and skewness-seeking behavior in a demographically diverse population By Douadia Bougherara; Lana Friesen; Céline Nauges
  9. Assessing External Validity in Practice By Sebastian Galiani; Brian Quistorff
  10. Where do I stand in the EU? Income comparisons and perceptions By Bublitz, Elisabeth; Jäger, Julian; Wang, Hequn; Beblo, Miriam; Lohmann, Henning
  11. The Governance of Non-Profits and their Social Impact: Evidence from a Randomized Program in Healthcare in the Democratic Republic of Congo By Anicet Fangwa; Caroline Flammer; Marieke Huysentruyt; Bertrand Quelin
  12. Cognitive Hierarchies in Multi-Stage Games of Incomplete Information By Po-Hsuan Lin
  13. Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial By Keshav Agrawal; Susan Athey; Ayush Kanodia; Emil Palikot
  14. On Welfare Analysis under Limited Attention By Mikhail Freer; Hassan Nosratabadi
  15. Efficient Market Hypothesis Test with Stock Tweets and Natural Language Processing Models By Bolin Mao; Chenhui Chu; Yuta Nakashima; Hajime Nagahara
  16. Bidding for Contracts under Uncertain Demand: Skewed Bidding and Risk Sharing By Yao Luo; Hidenori Takahashi

  1. By: Kerstin Grosch (Department of Economics, Vienna University of Economics and Business); Simone Haeckl (University of Stavanger); Martin G. Kocher (University of Vienna)
    Abstract: We examine individual-level determinants of interest in STEM and analyze whether a digital web application for elementary-school children can increase children's interest in STEM with a specific focus on narrowing the gender gap. Coupling a randomized-controlled trial with experimental lab and survey data, we analyze the effect of the digital intervention and shed light on the mechanisms. We confirm the hypothesis that girls demonstrate a lower overall interest in STEM than boys. Moreover, girls are less competitive and exhibit less pronounced math confidence than boys at the baseline. Our treatment increases girls' interest in STEM and decreases the gender gap via an increase in STEM confidence. Our findings suggest that an easy-to-implement digital intervention has the potential to foster gender equality for young children and can potentially contribute to a reduction of gender inequalities in the labor market such as occupational sorting and the gender wage gap later in life.
    Keywords: STEM, digital intervention, gender equality, field experiment
    JEL: C93 D91 I24 J16 J24
    Date: 2022–08
  2. By: Anujit Chakraborty (Department of Economics, University of California Davis)
    Abstract: This paper deploys a novel experiment to compare four theories that explain both selfish and non-selfish cooperation. The four theories capture incomplete information (à la Kreps et al. (1982)) alongside the following four non-selfish motives: caring about others (Altruism), being conscientious about cooperation (Duty), enjoying social-efficiency (Efficiency-Seeking), and reciprocity (Sequential Reciprocity). Our experimental design varies the decline-rate of future rewards, under which these theories make contrasting predictions. We find that Efficiency-Seeking is the other-regarding behavior that fits the experimental data best. A Finite Mixture Model analysis finds that 40-49% of our subjects are selfish, 36-45% are Efficiency-seeking, 1-4% are Duty players, and 6-20% are Altruistic.
    JEL: C72 C73 C92
    Date: 2022–09–15
  3. By: Brodeur, Abel; Cook, Nikolai M.; Hartley, Jonathan S.; Heyes, Anthony
    Abstract: Randomized controlled trials (RCTs) are increasingly prominent in economics, with pre-registration and pre-analysis plans (PAPs) promoted as important in ensuring the credibility of findings. We investigate whether these tools reduce the extent of p-hacking and publication bias by collecting and studying the universe of test statistics, 15,992 in total, from RCTs published in 15 leading economics journals from 2018 through 2021. In our primary analysis, we find no meaningful difference in the distribution of test statistics from pre-registered studies, compared to their non-pre-registered counterparts. However, pre-registered studies that have a complete PAP are significantly less p-hacked. These results point to the importance of PAPs, rather than pre-registration in itself, in ensuring credibility.
    Keywords: Pre-analysis plan,Pre-registration,p-Hacking,Publication bias,Research credibility
    JEL: B41 C13 C40 C93
    Date: 2022
  4. By: Elwyn Davies; Marcel Fafchamps
    Abstract: We conduct an interactive online experiment framed as an employment contract between employer and worker. Subjects from the US, India, and Africa are matched in pairs within and, in some cases, across countries. Employers make a one-period offer to a worker who can either decline or choose a high or low effort. The offer is restricted to be from a variable set of possible contracts: high and low fixed wage; bonus and malus contracts; and bonus and malus with reneging. High effort is always efficient. Self-interest predicts a fraction of observed choices, but many choices are better explained either by conditional reciprocity or by intrinsic motivation. Subjects from India and Africa are more likely to follow intrinsic motivation and they provide high effort more often. US subjects are more likely to follow self-interest and reach a less efficient outcome on average, but workers earn slightly more. We find no evidence that workers favor employers from some countries or that employers treat workers from different countries differently. Individual characteristics and stated attitudes toward worker incentives are unable to predict the behavioral differences observed between countries, thus allowing the possible existence of cultural differences in the response to labor incentives.
    JEL: D9 J31 O12 O57
    Date: 2022–08
  5. By: Brodeur, Abel; Cook, Nikolai; Heyes, Anthony
    Abstract: Amazon's Mechanical Turk is a very widely-used tool in business and economics research, but how trustworthy are results from well-published studies that use it? Analyzing the universe of hypotheses tested on the platform and published in leading journals between 2010 and 2020 we find evidence of widespread p-hacking, publication bias and over-reliance on results from plausibly under-powered studies. Even ignoring questions arising from the characteristics and behaviors of study recruits, the conduct of the research community itself erodes substantially the credibility of these studies' conclusions. The extent of the problems vary across the business, economics, management and marketing research fields (with marketing especially afflicted). The problems are not getting better over time and are much more prevalent than in a comparison set of non-online experiments. We explore correlates of increased credibility.
    Keywords: online crowd-sourcing platforms,Amazon Mechanical Turk,p-hacking,publication bias,statistical power,research credibility
    JEL: B41 C13 C40 C90
    Date: 2022
  6. By: John J. Conlon; Malavika Mani; Gautam Rao; Matthew W. Ridley; Frank Schilbach
    Abstract: We provide evidence of a powerful barrier to social learning: people are much less sensitive to information others discover compared to equally-relevant information they discover themselves. In a series of incentivized lab experiments, we ask participants to guess the color composition of balls in an urn after drawing balls with replacement. Participants' guesses are substantially less sensitive to draws made by another player compared to draws made themselves. This result holds when others' signals must be learned through discussion, when they are perfectly communicated by the experimenter, and even when participants see their teammate drawing balls from the urn with their own eyes. We find a crucial role for taking some action to generate one's `own' information, and rule out distrust, confusion, errors in probabilistic thinking, up-front inattention and imperfect recall as channels.
    JEL: D03 D83 D9 D91
    Date: 2022–08
  7. By: Lisa A. Gennetian; Greg Duncan; Nathan A. Fox; Katherine Magnuson; Sarah Halpern-Meekin; Kimberly G. Noble; Hirokazu Yoshikawa
    Abstract: A key policy question in evaluating social programs to address childhood poverty is how families receiving unconditional financial support would spend those funds. Economists have limited empirical evidence on this topic in the U.S. We provide causal estimates of financial and time investments in infants among families living in poverty from a large-scale, multi-site randomized controlled study of monthly unconditional cash transfers starting at the time of a child’s birth. We find that the cash transfers increased spending on child-specific goods and mothers’ early-learning activities with their infants. The marginal propensity to consume child-focused items from the cash transfer exceeded that from other income, consistent with the behavioral cues in the cash transfer design. We find no statistically detectable offsets in household earnings nor statistically detectable impacts in other pre-registered outcomes related to general household expenditures, maternal labor supply, infants’ time in childcare, or mothers’ subjective well-being.
    JEL: D13 H31 I30 J13 J18 J22
    Date: 2022–08
  8. 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); Lana Friesen (University of Queensland [Brisbane]); Céline Nauges (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - 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)
    Abstract: We study the interaction between risk-taking and skewness-seeking behavior among a demographically diverse sample of French adults using an experiment that elicits certainty equivalent over lotteries that vary the second and third moments orthogonally. We find that the most common behavior is risk avoidance and skewness seeking. On average, we find no interaction between the two, and a weakly significant interaction only in some segments of the population. That is, in most cases, skewness seeking is not affected by the variance of the lotteries involved, nor is risk taking affected by the skewness of the lotteries. We also find a significant positive correlation between risk-avoiding and skewness-seeking behavior. Older and female participants make more risk-avoiding and more skewness-seeking choices, while less educated people and those not in executive occupations are more skewness seeking. Estimated decision models show that utility curvature, likelihood sensitivity, and optimism can explain the observed behaviors.
    Keywords: Risk,Skewness,Certainty Equivalent,Experiment
    Date: 2022–09
  9. By: Sebastian Galiani; Brian Quistorff
    Abstract: We review, from a practical standpoint, the evolving literature on assessing external validity (EV) of estimated treatment effects. We provide an implementation and real-world assessment of the general EV measures developed in Bo and Galiani (2021). In the context of estimating conditional average treatment effect models for assessing external validity, we provide a novel method utilizing the Group Lasso (Yuan and Lin, 2006) to estimate a tractable regression-based model. This approach can perform better when settings have differing covariate distributions and allows for easily extrapolating the average treatment effect to new settings. We apply these measures to a set of identical field experiments conducted in three different countries (Galiani et al., 2017).
    JEL: C55
    Date: 2022–08
  10. By: Bublitz, Elisabeth; Jäger, Julian; Wang, Hequn; Beblo, Miriam; Lohmann, Henning
    Abstract: With a survey experiment conducted in Germany, Italy, Poland, and Sweden, we investigate how EU citizens rank themselves within the EU. In all four countries, (mis-)perceptions of EU income positions result primarily from respondents' (incomplete) information about their national position and their perceived country ranking within the EU. Low-income respondents tend to place themselves higher and richer respondents lower in both the national and EU income distribution. Respondents who are informed about their income position estimate their EU ranking more accurately in a follow-up survey. Our findings show that concepts of inequality at the EU level are empirically meaningful and that EU citizens have a reference frame beyond their own country.
    Keywords: Perceptions,Inequality,European Union,Survey Experiment
    JEL: C91 D31 H24
    Date: 2022
  11. By: Anicet Fangwa; Caroline Flammer; Marieke Huysentruyt; Bertrand Quelin
    Abstract: How can non-profit organizations improve their governance to increase their social impact? This study examines the effectiveness of a bundle of governance mechanisms – consisting of social performance-based incentives combined with auditing and feedback – in the context of a randomized governance program conducted in the Democratic Republic of Congo's healthcare sector. Within the program, a set of health centers were randomly assigned to a governance treatment while others were not. We find that the governance treatment leads to i) higher operating efficiency and ii) improvements in health outcomes (measured by a reduction in stillbirths and neonatal deaths). Furthermore, we find that funding is not a substitute for governance – health centers that only receive funding increase their scale, but do not show improvements in operating efficiency nor health outcomes. Overall, our results suggest that governance plays an important role in achieving the non-profits' objectives and increasing the social impact of the funds invested.
    JEL: I0 I1 I2 O1
    Date: 2022–08
  12. By: Po-Hsuan Lin
    Abstract: We explore the dynamic cognitive hierarchy (CH) theory proposed by Lin and Palfrey (2022) in the setting of multi-stage games of incomplete information. In such an environment, players will learn other players' payoff-relevant types and levels of sophistication at the same time as the history unfolds. For a class of two-person dirty faces games, we fully characterize the dynamic CH solution, predicting that lower-level players will figure out their face types in later periods than higher-level players. Finally, we re-analyze the dirty faces game experimental data from Bayer and Chan (2007) and find the dynamic CH solution can better explain the data than the static CH solution.
    Date: 2022–08
  13. By: Keshav Agrawal; Susan Athey; Ayush Kanodia; Emil Palikot
    Abstract: We study the impact of personalized content recommendations on the usage of an educational app for children. In a randomized controlled trial, we show that the introduction of personalized recommendations increases the consumption of content in the personalized section of the app by approximately 60% and that the overall app usage increases by 14%, compared to the baseline system of stories selected by content editors for all students. The magnitude of individual gains from personalized content increases with the amount of data available about a student and with preferences for niche content: heavy users with long histories of content interactions who prefer niche content benefit more than infrequent, newer users who like popular content. To facilitate the diffusion of personalized recommendation systems, we provide a framework for using offline data to develop such a system.
    Date: 2022–08
  14. By: Mikhail Freer; Hassan Nosratabadi
    Abstract: An observer wants to understand a decision-maker's preferences from her choice. She believes that decision-maker takes decisions under limited attention; i.e., does not consider all alternatives. In this paper, we make the point that given the nature of established experimental evidence, the existing models of limited attention are not quite helpful in fulfilling the observer's goal. Addressing this challenge, we propose a ``minimal'' adjustment to the theory of choice under limited attention by assuming that decision-maker makes at least one comparison in her decision-process. We illustrate that, as minimal as this adjustment is, it enriches the model with significant welfare relevance. We further apply our model to experimental data and establish that this significant increase in welfare-relevance comes with negligible costs in explanatory power.
    Date: 2022–08
  15. By: Bolin Mao (Kyoto Institute of Economic Research, Kyoto University); Chenhui Chu (Graduate School of Informatics, Kyoto University); Yuta Nakashima (Institute for Datability Science, Osaka University); Hajime Nagahara (Institute for Datability Science, Osaka University)
    Abstract: The efficient market hypothesis (EMH) plays a fundamental role in modern financial theory. Previous empirical studies have tested the weak and semi-strong forms of EMH with typical financial data, such as historical stock prices and annual earnings. However, few tests have been extended to include alternative data such as tweets. In this study, we use 1) two stock tweet datasets that have different features and 2) nine natural language processing (NLP)-based deep learning models to test the semi-strong form EMH in the United States stock market. None of our experimental results show that stock tweets with NLP-based models can prominently improve the daily stock price prediction accuracy compared with random guesses. Our experiment provides evidence that the semi-strong form of EMH holds in the United States stock market on a daily basis when considering stock tweet information with the NLP-based models.
    Keywords: Efficient Market Hypothesis Test, Daily Stock Price Prediction, Stock Tweet, Natural Language Processing
    JEL: C4 C5 G1
    Date: 2022–09
  16. By: Yao Luo; Hidenori Takahashi
    Abstract: Procurement projects often involve substantial uncertainty in inputs at the time of contracting. Whether the procurer or contractor assumes such risk depends on the specific contractual agreement. We develop a model of auction contracts where bidders have multidimensional private information. Bidders balance skewed bidding and risk exposure; both efficient and inefficient bidders submit a low bid via skewed bidding. We document evidence of i) risk-balancing behavior through bid portfolio formation and ii) opportunistic behavior via skewed bidding using auction data. Counterfactual experiments suggest the onus of bearing project risk should fall on the procurer (contractor) when project risk is large (small).
    Keywords: Contract, Unit-Price, Fixed-Price, Portfolio, Cost Overrun, Procurement, Scoring Auction
    JEL: L5
    Date: 2022–09–01

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