Date: March 28th, 2024 (12:30 pm – 1:30 pm)

Speaker: Maria Abascal

Paper Title: “Diversity and Prosocial Behavior Across NYC Neighborhoods: Evidence from a Lost Wallet Experiment” with Shannon Rieger (NYU) and Delia Baldassarri (NYU)

Abstract: How is prosocial behavior toward strangers affected by context? Urban scholars have traditionally emphasized the detrimental role of socioeconomic deprivation and physical and social disorder. Recent work has shifted attention to ethnoracial diversity. However, design and data limitations curb the inferential capacity of previous studies, especially with respect to the negative effect of ethnoracial diversity on prosocial behavior. We use a lost wallet experiment to improve on past studies by (1) sampling neighborhoods in order to distinguish ethnoracial heterogeneity from minority share and socioeconomic status, and (2) specifying the intended target of prosocial behavior, which we accomplish by experimentally manipulating the race/ethnicity, socioeconomic status, and prosocial inclination of the wallet owner. Roughly 1,860 wallets were distributed in 62 purposively sampled NYC neighborhoods in the spring of 2023. We find that ethnoracial diversity is weakly positively related to return attempts, whereas observed neighborhood disorder and socioeconomic deprivation are negatively related to return attempts. Recipients from different ethnoracial and socioeconomic backgrounds are neither more nor less likely to elicit return attempts, but recipients who exhibit a prosocial inclination themselves elicit more such attempts. Our findings underscore the importance of the contextual factors highlighted by a rich tradition of urban research while casting doubt on the provocative claim that ethnoracial diversity per se undermines prosocial behavior. 

Bio:

Maria Abascal is an Associate Professor of Sociology at New York University. She received her PhD in Sociology and Social Policy from Princeton University, and completed a postdoc in the Population Studies and Training Center at Brown University.

Broadly, she is interested in intergroup relations and boundary processes, especially as they pertain to race, ethnicity, and nationalism. Most of her research explores the impact of demographic diversification—real and perceived—on intergroup relations in the United States. She draws on a range of quantitative methods and data sources, including original lab, survey, and field experiments.

Ongoing projects examine the relationship between racial/ethnic diversity and cooperation, boundary-drawing in the wake of diversification, and lay understandings of “diversity.”

Date: April 4th, 2024 (12:30 pm – 1:30 pm)

Speaker: Björn Bartling

Paper Title: “Free to Fail? Paternalistic Preferences in the United States” with Alexander Cappelen (Norwegian School of Economics), Henning Hermes (info Institute Munich), Marit Skivenes (University of Bergen) and Bertil Tungodden (NHH)

Abstract: We study paternalistic preferences in two large-scale experiments with participants from the general population in the United States. Spectators decide whether to intervene to prevent a stakeholder, who is mistaken about their choice set, from making a choice that is not aligned with the stakeholders’ own preferences. We find causal evidence for the nature of the intervention being of great importance for the spectators’ willingness to intervene. Only a minority of the spectators implement a hard intervention that removes the stakeholder’s freedom to choose, while a large majority implement a soft intervention that provides information without restricting the choice set. This finding holds regardless of the stakeholder’s responsibility for being mistaken about the choice set – whether the source of mistake is internal or external – and in different subgroups of the population. We introduce a theoretical framework with two paternalistic types – libertarian paternalists and welfarists – and show that the two types can account for most of the spectator behavior. We estimate that about half of the spectators are welfarists and that about a third are libertarian paternalists. Our results shed light on attitudes toward paternalistic policies and the broad support for soft interventions.

Bio:

Björn Bartling is Professor of Economics at the University of Zurich and Vice Chairman of the Department of Economics. In his research, he uses empirical methods to study the impact of social and moral motivations in economic contexts. 

Professor Bartling is also a Visiting Professor at the Centre for Experimental Research on Fairness, Inequality and Rationality (FAIR) – The Choice Lab, NHH Norwegian School of Economics, and serves as Associate Editor for the Journal of the European Economic Association and for Management Science.

Date: April 11th, 2024 (12:30 pm – 1:30 pm)

Speaker: Shachar Kariv

Paper Title: “The Predictivity and Falsifiability of Theories of Choice Under Uncertainty” with with  Keaton Ellis (UC Berkeley), and Erkut Ozbay, (University of Maryland)

Abstract: Economic models are founded on parsimony and interpretability, which is achieved through axioms on choice behavior. We empirically evaluate the predictive accuracy of economic models of choice under risk and ambiguity, and the strength of their axiomatic foundations, using complementary methods of completeness (Fudenberg et al., 2022) and restrictiveness (Fudenberg et al., 2023), respectively. To better understand the tradeoff between the two concepts, we additionally relate their performance to machine learning models. We use budgetary choice environments with three dimensions to provide a strong test of axioms. We show that adding a third dimension of choice marginally reduces completeness of economic models, but significantly increases restrictiveness. Economic models are also more complete than machine learning models, and are significantly more restrictive. These results are robust to considering an environment of choice under ambiguity than choice under risk.

Bio:

Shachar Kariv is a Benjamin N. Ward Professor of Economics. He was educated at Tel Aviv University and New York University, where he received my Ph.D. in economics in 2003, the same year he joined the Department of Economics at the University of California, Berkeley. He has been the Department Chair (2014-17 and 2021-22) the Faculty Director of UC Berkeley Experimental Social Science Laboratory (2009-2014), aka Xlab, a laboratory for conducting experiment-based investigations of issues of interest to social sciences.  

Shachar was a visiting member of the School of Social Science at the Institute for Advanced Studies at Princeton (2005-6), a visiting professor at the European University Institute (2008), a visiting fellow at Nuffield Collegeof the University of Oxford (2009), a visiting professor at the Interdisciplinary Center (IDC) Herzliya (2011-12), and a visiting professor at the Department of Economics at Stanford University (2014). I am also a visiting professor (Professor II) at the Department of Economics at the NHH Norwegian School of Economics where he is affiliated with the Choice Lab.

Shachar is also the recipient of the UC Berkeley Haas School of Business Cheit Award for Excellence in Teaching (2012), the UC Berkeley Division of Social Sciences Distinguished Teaching Award (2008), and the Graduate Economics Association Outstanding Advising Award (2006). And was also awarded NYU College of Arts and Science Outstanding Teaching Award (Golden Dozen) in recognition of excellence in teaching and contributions to undergraduate education (2002) and NYU Dean’s Outstanding Teaching Award in the Social Sciences (2001). For his Ph.D. dissertation at NYU, he received the Outstanding Dissertation Award in the Social Sciences (2003), and was also awarded a Sloan Research Fellowship for Economics (2009-10).

Date: April 18th, 2024 (12:30 pm – 1:30 pm)

Speaker: Andrew Gelman

Paper Title: “How large is that treatment effect, really?”

Abstract: “Unbiased estimates” aren’t really unbiased, for a bunch of reasons, including aggregation, selection, extrapolation, and variation over time.  Econometrics typically focus on causal identification, with this goal of estimating “the” effect.  But we typically care about individual effects (not “Does the treatment work?” but “Where and when does it work?” and “Where and when does it hurt?”).  Estimating individual effects is relevant not only for individuals but also for generalizing to the population.  For example, how do you generalize from an A/B test performed on a sample right now to possible effects on a different population in the future?  Thinking about variation and generalization can change how we design and analyze experiments and observational studied.  We demonstrate with examples in social science and public health.

Bio:

Andrew Gelman (PhD, Harvard, 1990) is Higgins Professor of Statistics, Professor of Political Science, and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for the best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of forty.

Professor Gelman’s research spans a wide range of topics, including why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City; the statistical challenges of estimating small effects; the probability that one vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in home basements; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

Date: November 30th, 2023 (12:30 pm – 1:30 pm)

Speaker: Marie Claire Villeval

Paper Title: “Selective Information Sharing and Group Delusion”

Abstract: Although in many situations groups make better decisions than individuals, groups also often make mistakes. We investigate experimentally one possible source
of sub-optimal decision-making by groups: the selective and asymmetric sharing of ego-relevant information among team members. We show that good news about
one’s performance is shared more often with team members than bad news. The biased information sharing within teams, together with selection neglect by the
receivers, induces higher team confidence compared to an unbiased exchange of performance feedback. As a result, weak teams end up making worse investment
decisions in a bet whose success depends on the team ability. The endogenous social exchange of ego-relevant information may thus lead to detrimental group delusion.

Bio: Marie Claire Villeval’s main research interests are behavioral and experimental economics applied to the analysis of incentives, social norms, ethics and dishonesty, motivated beliefs and biases in the transmission of information. She is Research Professor at the National Center for Scientific Research (CNRS) and the head of GATE-Lab at the University of Lyon, France. She is the Past-President of the Economic Science Association (ESA) and the founding President of the French association of experimental economics (ASFEE). She is a Fellow of the European Association of Labour Economists (EALE) and a member of the Academia Europaea, IZA and GLO. She has been awarded the Silver Medal of CNRS in 2017. She is Department Editor at Management Science.

Date: November 16th, 2023 (12:30 pm – 1:30 pm)

Speaker: Marina Agranov

Paper Title: “Disentangling Suboptimal Updating: Complexity, Structure, and Sequencing”

Abstract: We study underlying reasons for the failure of individuals to adhere to Bayes’ rule and decompose this departure into three elements: (i) task complexity, (ii) information structure, and (iii) timing of information release. In a series of controlled experiments, we systematically alter all three elements and quantify their magnitude. We address task complexity by introducing a novel connection to nonlinear calculations required for Bayesian updating. We experimentally explore this link and find empirical support for it.

Bio: Marina Agranov is an experimental economist specializing in theory-based experiments. Marina’s recent work includes bargaining games, social learning environments, games on networks, agency problems, auctions, and individual decision-making under risk. Marina is a Full Professor of Economics at Caltech, the Research Associate at the National Bureau of Economic Research (NBER), and the Director of the Center for Theoretical and Experimental Social Sciences at Caltech (CTESS).

Date: November 2nd, 2023 (12:30 pm – 1:30 pm)

Speaker: Jimena Galindo

Paper Title: “Learning with Misspecified Models: The Case of Overestimation“

Abstract: I design a framework and a laboratory experiment that allow for the comparison of multiple theories of misspecified learning. I focus on a framework with endogenous information and a data-generating process ruled by two fundamentals: an ego-relevant parameter and a state. Within this framework, I study three forces that can lead to misspecified beliefs: initial misspecifications, learning traps, and biased updating. I find that biased updating is the main driver of misspecified beliefs in the lab. In addition, I vary the degree of ego relevance of the parameter by introducing a stereotype treatment. The data are consistent with biased updating in both cases but for different reasons: when learning about themselves, subjects attribute successes to their own ability and failures to luck. Instead, in the stereotype treatment, they compensate for initial negative biases by over-attributing positive signals to the ability of others. This tendency translates into similar observed choices but different dynamics in beliefs. 

Date: October 19th, 2023 (12:30 pm – 1:30 pm)

Speaker: WonSeok Yoo

Paper Title: “Belief Updating Under Cognitive Dissonance: An Experimental Study”

Abstract: Cognitive dissonance is the psychological tension that arises from holding two conflicting beliefs, views, or actions simultaneously. We investigate the dynamics of belief updating in the face of such cognitive dissonance, particularly within financial decision making contexts. By introducing transaction costs in a controlled laboratory setting, we are able to simulate conditions that naturally induce cognitive dissonance among participants. Within our experimental design, subjects are initially endowed with one of two potential assets at random. They are then asked to report their beliefs regarding their assigned asset after a series of signals. For particular cases, they subsequently face a decision: to either switch or keep their initial asset. Our findings present evidence that the imposition of transaction costs deters individuals from “switching” compared to Bayesian benchmark. Moreover, individuals distort their beliefs to better justify their sub-optimal decision to retain their original asset in an effort to align their beliefs with their actions and alleviate cognitive dissonance.e a causal relationship between judicial institutions and economic development.

Date: October 5th, 2023 (12:30 pm – 1:30 pm)

Speaker: Daniel Chen

Paper Title: “Data Science for Justice: Evidence from a Nationwide Randomized Experiment in Kenya”

Abstract: Can data science improve the functioning of courts, and unlock the positive effects of institutions on development? In a nationwide experiment in Kenya, we use algorithms to identify the greatest sources of court delay for each court and recommend actions. We randomly assign courts to receive no information, information, or an information and accountability intervention. Information and accountability reduces case duration by 22%. Using continuous household surveys, we find that in regions with treated courts, workers were more likely to have formal contracts and higher wages, especially in contract-intensive industries. These results demonstrate a causal relationship between judicial institutions and economic development.

Bio:

Daniel Li Chen is Director of Research at the CNRS and Professor at the Toulouse School of Economics. He is also a Senior Fellow at the IAST and the founder of oTree Open Source Research Foundation and Data Science Justice Collaboratory. Chen was previously Chair of Law and Economics and co-founder of Law and Economics Center at ETH; he was a tenure-track assistant professor in Law (primary), Economics, and Public Policy at Duke University. He received his BA (Summa Cum Laude, Phi Beta Kappa) and MS from Harvard University in Applied Mathematics and Economics; completed his Economics PhD from MIT; and obtained a JD from Harvard Law School. 

 Chen uses his extensive empirical training to tackle long standing legal questions previously difficult to empirically analyze. He has attained prominence through the development of open source tools to study human behavior and through large-scale empirical studies – data science, artificial intelligence, and machine learning – on the relationship between law, social norms and the enforcement of legal norms, and on judicial systems.

Date: September 21st, 2023 (12:30 pm – 1:30 pm)

Speaker: Alex Imas

Paper Title: “Over- and Underreaction to Information”

Abstract: This paper explores how properties of the learning environment determine how people react to information. We develop a two-stage model of belief formation where people first reduce complexity by channeling attention to a subset of states that are representative of the observed information, then evaluate this information using Bayes’ rule subject to cognitive imprecision. The model predicts overreaction when environments are complex, signals are noisy, or priors are concentrated on intermediate states; it predicts underreaction when environments are simple, signals are precise, or priors concentrated on more extreme states. Results from a series of pre-registered experiments provide direct support for these predictions, as well as the proposed attentional mechanism. We show that the two-stage model is highly complete in capturing explainable variation in belief-updating; in particular, the interaction between the two psychological mechanisms is critical to explaining belief-formation in more complex settings. These results connect disparate findings in prior work: underreaction is typically found in laboratory studies, which feature simple learning settings, while overreaction is prevalent in financial markets, which feature more complex environments.

Bio: Alex Imas is a Professor of Behavioral Science and Economics at the University of Chicago Booth School of Business. He studies behavioral economics with a focus on dynamic decision-making. His research explores topics related to choice under uncertainty, discrimination, mental representation, and how people learn from information. Alex Imas’ work utilizes a variety of methods, including lab experiments, field experiments, analysis of observational data and theoretical modeling.