Discussion Paper No. 447
November 9, 2023
Reputational Concerns and Advice-Seeking at Work
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Abstract:
We examine the impact of reputational concerns on seeking advice. While seeking can improve performance, it may affect how others perceive the seeker's competence. In an online experiment with white-collar professionals (N=2,521), we test how individuals navigate this tradeoff and if others' beliefs about competence change it. We manipulate visibility of the decision to seek and stereotypes about competence. Results show a sizable and inefficient decline in advice-seeking when visible to a manager. Higher-order beliefs about competence cannot mediate this inefficiency. We find no evidence that managers interpret advice-seeking negatively, documenting a misconception that may hinder knowledge flows in organizations.
Keywords:
advice-seeking; reputational concerns; stereotypes; higher-order beliefs; knowledge flows; experiment;
JEL-Classification:
D16; D21; D83; D91; M51;
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Discussion Paper No. 440
October 30, 2023
Logic Mill - A Knowledge Navigation System
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Abstract:
Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a generalpurpose tool for future research applications in the social sciences and other domains.
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JEL-Classification:
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Discussion Paper No. 439
Ruled by Robots: Preference for Algorithmic Decision Makers and Perceptions of Their Choices
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As technology-assisted decision-making is becoming more widespread, it is important to understand how the algorithmic nature of the decisionmaker affects how decisions are perceived by the affected people. We use a laboratory experiment to study the preference for human or algorithmic decision makers in re-distributive decisions. In particular, we consider whether algorithmic decision maker will be preferred because of its unbiasedness. Contrary to previous findings, the majority of participants (over 60%) prefer the algorithm as a decision maker over a human—but this is not driven by concerns over biased decisions. Yet, despite this preference, the decisions made by humans are regarded more favorably. Participants judge the decisions to be equally fair, but are nonetheless less satisfied with the AI decisions. Subjective ratings of the decisions are mainly driven by own material interests and fairness ideals. For the latter, players display remarkable flexibility: they tolerate any explainable deviation between the actual decision and their ideals, but react very strongly and negatively to redistribution decisions that do not fit any fairness ideals. Our results suggest that even in the realm of moral decisions algorithmic decision-makers might be preferred, but actual performance of the algorithm plays an important role in how the decisions are rated.
Keywords:
delegation; algorithm aversion; redistribution; fairness;
JEL-Classification:
C91; D31; D81; D9; O33;
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Discussion Paper No. 438
Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making
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Are people algorithm averse, as some previous literature indicates? If so, can the retention of human oversight increase the uptake of algorithmic recommendations, and does keeping a human in the loop improve accuracy? Answers to these questions are of utmost importance given the fast-growing availability of algorithmic recommendations and current intense discussions about regulation of automated decision-making. In an online experiment, we find that 66% of participants prefer algorithmic to equally accurate human recommendations if the decision is delegated fully. This preference for algorithms increases by further 7 percentage points if participants are able to monitor and adjust the recommendations before the decision is made. In line with automation bias, participants adjust the recommendations that stem from an algorithm by less than those from another human. Importantly, participants are less likely to intervene with the least accurate recommendations and adjust them by less, raising concerns about the monitoring ability of a human in a Human-in-the-Loop system. Our results document a trade-off: while allowing people to adjust algorithmic recommendations increases their uptake, the adjustments made by the human monitors reduce the quality of final decisions.
Keywords:
automated decision-making; algorithm aversion; algorithm appreciation; automation bias;
JEL-Classification:
O33; C90; D90;
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Discussion Paper No. 434
October 24, 2023
Self-preferencing, Quality Provision, and Welfare in Mobile Application Markets
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Abstract:
Platforms often display their products ahead of third-party products in search. Is this due to consumers preferring platform-owned products or platforms engaging in self-preferencing by biasing search towards their own products? What are the welfare implications? I develop a structural model of mobile application markets to identify self-preferencing and quantify its welfare effects, taking into account third-party developers' quality adjustment. A new dataset on app downloads, prices, characteristics, and search rankings is used to estimate the model. Estimates indicate self-preferencing. Simulations show higher consumer welfare and third-party profits without self-preferencing.
Keywords:
competition policy; platform design; consumer search; endogenous product choice;
JEL-Classification:
D12; D83; L13; L86;
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Discussion Paper No. 432
October 19, 2023
Monopsony and Automation
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We examine the impact of labor market power on firms' adoption of automation technologies. We develop a model that incorporates labor market power into the task-based theory of automation. We show that, due to higher marginal cost of labor, monopsonistic firms have stronger incentives to automate than wage-taking firms, which could amplify or mitigate the negative employment effects of automation. Using data from US commuting zones, our results show that commuting zones that are more exposed to industrial robots exhibit considerably larger reductions in both employment and wages when their labor markets demonstrate higher levels of concentration.
Keywords:
automation; employment; labor market concentration; industrial robots; wage setting;
JEL-Classification:
J23; J30; J42; L11; O33;
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Discussion Paper No. 419
August 25, 2023
Allegations of Sexual Misconduct, Accused Scientists, and Their Research
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Does the scientific community sanction sexual misconduct? Using a sample of scientists accused of sexual misconduct at US universities, we find that their prior work is cited less after allegations surface. The effect weakens with distance in the coauthorship network, indicating that researchers learn about allegations through their peers. Among the closest peers, male authors react more strongly, suggesting that they feel a greater need to disassociate themselves from the accused. In male-dominated fields, the effects on citations are more muted. Accused scientists are more likely to leave academic research, to move to non-university institutions, and to publish less.
Keywords:
sexual misconduct; scientific community; scientific impact;
JEL-Classification:
J16; M14; I23; K4;
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Discussion Paper No. 418
Structural Shocks and Political Participation in the US
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This paper examines the impact of the large structural shocks -- automation and import competition -- on voter turnout during US federal elections from 2000 to 2016. Although the negative income effect of both shocks is comparable, we find that political participation decreases significantly in counties more exposed to industrial robots. In contrast, the exposure to rising import competition does not reduce voter turnout. A survey experiment reveals that divergent beliefs about the effectiveness of government intervention drive this contrast. Our study highlights the role of beliefs in the political economy of technological change.
Keywords:
automation; trade; labor demand; voter turnout;
JEL-Classification:
D72; J23; F16;
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Discussion Paper No. 417
August 23, 2023
The Behavioral Additionality of Government Research Grants
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There are different forms of public support for industrial R&D. Some attempt to increase innovation by prompting firms to undertake more challenging projects than they would otherwise do. Access to a dataset from one such program, the Austrian Research Promotion Agency, allows me to examine the effect of research grants on firms' patenting outcomes. My estimates suggest that a government research grant increases the propensity to file a patent application with the European Patent Office by around 12 percentage points. Stronger effects appear for more experienced firms of advanced age. Additional evidence indicates that grants induce experienced firms to develop unconventional patents and patents that draw on knowledge novel to the firm. I interpret the findings in a "exploration vs. exploitation" model, in which grants are targeted at ambitious projects that face internal competition from more conventional projects within firms. The model shows that this mechanism is more salient in experienced firms, leading to a stronger response in behavior for this group of firms.
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JEL-Classification:
O38;
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Discussion Paper No. 415
August 10, 2023
Whom to Inform about Prices? Evidence from the German Fuel Market
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Combining a theoretical model of imperfect information with empirical evidence, we show how the effect of providing price information to consumers depends on how well informed they are beforehand. Theoretically, an increase in consumer information decreases prices more, the fewer ex ante informed consumers there are. Empirically, we study mandatory price disclosure in the German fuel market for two fuel types that differ in ex ante consumer information. The decline in prices is stronger when there are fewer ex ante informed consumers. The magnitude of the treatment effect declines over time but is intensified by local follow-on information campaigns.
Keywords:
mandatory price disclosure; consumer information; retail fuel market;
JEL-Classification:
D83; L41;