Mitigating manipulation in committees: Just let them talk!
[Working Paper]
[Preregistration]
Keywords: Group Decision Making, Committees, Manipulation, Face-to-face Communication, Delphi Technique
Many decisions rest on the collective judgment of small groups like committees or teams. However,
some group members may have hidden agendas and manipulate this judgment to influence decisions in
their favor. Utilizing an incentivized experiment, I compare how objective accuracy and perceived
trustworthiness of judgments from groups using the Delphi technique versus face-to-face interaction
are affected by manipulation.
Without manipulation, Delphi is more accurate. With manipulation, face-to-face is more accurate.
Only in situations with manipulation the more accurate interaction format is perceived as more
trustworthy. With manipulation, sharing of (truthful) information decreases in Delphi but not in
face-to-face interaction.
Debt Aversion: Theory and Measurement
[Working Paper]
Keywords: Debt Aversion, Intertemporal Choice, Risk and Time Preferences
Debt aversion can have severe adverse effects on financial decision-making. We propose a model of debt aversion, and design an experiment involving real debt and saving contracts, to elicit and jointly estimate debt aversion with preferences over time, risk and losses. Structural estimations reveal that the vast majority of participants (89%) are debt averse, and that this has a strong impact on choice. We estimate the "borrowing premium" -- the compensation a debt averse person would require to accept getting into debt -- to be around 16% of the principal for our average participant.
The debt aversion survey module: An experimentally validated tool to measure individual debt
aversion
[Working Paper]
Keywords: Debt Aversion, Preference Measurement, Survey Validation
We develop an experimentally validated, short and easy-to-use survey module for measuring individual debt aversion. To this end, we first estimate debt aversion on an individual level, using choice data from Meissner and Albrecht (2022). This data also contains responses to a large set of debt aversion survey items, consisting of existing items from the literature and novel items developed for this study. Out of these, we identify a survey module comprising two qualitative survey items to best predict debt aversion in the incentivized experiment.
#ManyDaughters: Multi-Analyst Project Exploring the Effect of Daughters on Preferences and Behaviors
We collect pre-analysis plans and analysis code from 100+ research teams testing hypotheses about the impact of having daughters on attitudes, preferences, and behaviors using data from the German Socioeconomic Panel. The main aim of this study is twofold. First, by varying whether research teams have access to a 5\% sample of the data before writing their pre-analysis plan, we explore whether initial access to sample data improves the quality of the pre-analysis plan and analysis compared to writing a pre-analysis plan without access to any data. Second, the study aims to further our understanding of the impact of having daughters on behaviors and preferences by conducting a meta-analysis of the test results suggested by participating research teams.
Lab² Many Labs study - ML²
We run seminal economic experiments in 50+ labs around the world to assess population heterogeneity, provide high-powered tests of key methodological issues and conduct replications for key findings from prominent papers.
Debt aversion among German households
We investigate the relation of individual debt attitudes to other socio-economic characteristics and its predictive power for real-world behavior based on a German representative sample.
Expanding Moral Wiggle Room: A Large-Scale Online Replication and the Role of Moral Context
We replicate and extend the influential research on "moral wiggle room" demonstrating how reduced transparency in dictator games enables self-interested behavior through willful ignorance.
Examining the generalizability of research findings from archival data
[Proceedings of the National Academy of Sciences, 2022]
Keywords: Generalizability, Archival Data, Reproducibility, Strategic Management, Forecasting