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Ongoing Project Presentation


15/03/2018 18:00-19:00


Title: Round it up: Preferences Exists for Rounded Totals (PERT)


Authors: Varun Sharma (Bocconi University), Zachary Estes (Bocconi University), Aradhna Krishna (University of Michigan)


Where: 4 E4 SR03  

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01/03/2018 18:00-19:00



Title: We Don't Know What We Don't Know: When and How the Use of Twitter's Public APIs Biases Scientific Inference


Authors:Rebekah Tromble,  Andreas Storz and Daniela Stockmann (Leiden University - Department of Political Science) 


Where: 4 E4 SR03  


Abstract. Though Twitter research has proliferated, no standards for data collection have crystallized. When using keyword queries, the most common data sources—the Search and Streaming APIs—rarely return the full population of tweets, and scholars do not know whether their data constitute a representative sample. This paper seeks to provide the most comprehensive look to-date at the potential biases that may result. Employing data derived from four identical keyword queries to the Firehose (which provides the full population of tweets but is cost-prohibitive), Streaming, and Search APIs, we use Kendall’s-tau and logit regression analyses to understand the differences in the datasets, including what user and content characteristics make a tweet more or less likely to appear in sampled results. We find that there are indeed systematic differences that are likely to bias scholars’ findings in almost all datasets we examine, and we recommend significant caution in future Twitter research.

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Working Paper Presentation


22/02/2018 17:00-18:00


Title: Divergence between Hypothetical and Consequential Dependent Measures 


Authors: Ioannis Evangelidis (Bocconi University), Deb Small (Wharton, University of Pennsylvania), 

Jonathan Levav (Stanford Graduate School of Business)


Where: 4 E4 SR03  


Abstract. In this paper, we empirically assess discrepancies resulting from the use of real versus hypothetical dependent measures in studies of human behavior. We focus on two behaviors: donations and decision-making under risk. In four well-powered studies (total N = 3,218), we present participants with identical stimuli and ask them to respond to either hypothetical or consequential measures. We observe two empirical regularities. First, we find that the dependent measure has a systematic effect on the behavior that it intends to measure. Participants are more likely to donate and more likely to assume risk when the dependent measure is hypothetical compared to real. Second, we find a contingency between the decision context and the dependent measure in the context of donation behavior but not in the context of risky decisions. Changes in the decision context—in particular introducing new choice alternatives—are likely to produce divergent effects in hypothetical versus real measures of donation—but not of risky—behavior. We conclude with a discussion about the implications of our results for behavioral researchers.

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Working Paper Presentation 


23/11/2017 17:00-18:00


Title: Gender Gaps in Math Tests: Women under Pressure


Authors: Vincenzo Galasso (Bocconi University, Dondena, IGIER and CEPR), Paola Profeta (Bocconi University, Dondena)


Where: 4 E4 SR03  


Abstract: Gender gaps exist in math tests, particularly among high performing students. What are the determinants of this gap? In this paper, we want to investigate the role of time pressure. This is an important and policy-relevant channel, since all math tests in which gender gaps emerge (SAT, GRE, PISA, entry tests for college or business schools) are performed under tight time constraints. To explore this channel, we run a lab experiment with 287 Italian undergraduate and Master students at Bocconi University. Given our subject pool, we expect large gender differences, since gender gaps in math are large in Italy and among high performers. In our experiment, each student performs four incentivized tests: a test of attentiveness, a working memory test, a math test under no time pressure and a math test under (low or high) time pressure. We find that a gender gap emerges even in untimed math test: on average male students provide half (out of 10) more correct answers than females and the ratio of male to female top performers (i.e., students with all correct answers) is 1.8. The gender gap increases under low pressure: on average male students have one more correct answers than females and the ratio of male to female top performers is 2.4. The gender gap increases even further under high pressure: while both males and females perform worse under pressure, on average male students provide 1.25 more correct answers than females. Moreover, the ratio of male to female top performers jumps to 4. These results suggest that half of the gender gap observed in math tests performed under tight time constraints can be accounted for by a different gender response to time pressure.


Last update 01 October 2018 - 16:08:34