Discussion Paper No. 448
November 9, 2023
Mimicking the Opposition: Bismarck's Welfare State and the Rise of the Socialists
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Abstract:
This paper examines the consequences of a government mimicking the policy of its competitor by studying the introduction of the welfare state in 19th century Germany. The reform conducted by the conservative government targeted blue-collar workers and aimed to reduce the success of the socialist party. The result based on a difference-in-differences design shows that the socialist party benefited in elections due to the reform. The analysis of the mechanism points to the socialist's issue ownership by strengthening its reform orientation, which voters followed. The results are not driven by other political and economic channels related to the reform.
Keywords:
welfare state; socialism; government; opposition; issue ownership; voting behavior; Germany;
JEL-Classification:
D74; H53; I38; N44; P16;
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Discussion Paper No. 447
Reputational Concerns and Advice-Seeking at Work
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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. 446
Round-Number Effects in Real Estate Prices: Evidence from Germany
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Abstract:
Round numbers affect behavior in various domains, e.g., as prominent thresholds or focal points in bargaining. In line with earlier findings, residential real estate transactions in Germany cluster at round-number prices, but there are also interesting (presumably cultural) differences. We extend our analysis to the commercial real estate market, where stakes are even higher and market participants arguably more experienced. For the same type of object, professionals cluster significantly less on round-number prices compared to non-professionals. We employ machine learning and show that transactions of family homes and condominiums at round-number prices are 2–7% above their hedonic values.
Keywords:
round-number effects; focal points; residential real estate; commercial real estate; housing prices; machine learning;
JEL-Classification:
D01; D91; C78; R31;
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Discussion Paper No. 445
The Efficacy of Tournaments for Non-Routine Team Tasks
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Tournaments are often used to improve performance in innovation contexts. Tournaments provide monetary incentives but also render teams' identity and image concerns salient. We study the effects of tournaments on team performance in a non-routine task and identify the importance of these behavioral aspects. In a field experiment (n>1,700 participants), we vary the salience of team identity, social image concerns, and whether teams face monetary incentives. Increased salience of team identity does not improve performance. Social image motivates the top performers. Additional monetary incentives improve all teams' outcomes without crowding out teams' willingness to explore or perform similar tasks again.
Keywords:
team work; tournaments; rankings; incentives; identity; image concerns; innovation; exploration; natural field experiment;
JEL-Classification:
C93; D90; J24; J33; M52;
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Discussion Paper No. 444
Deceptive Communication: Direct Lies vs. Ignorance, Partial-Truth and Silence
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In cases of conflict of interest, people can lie directly or evade the truth. We analyse this situation theoretically and test the key behavioural predictions in a novel sender-receiver game. We find senders prefer to deceive through evasion rather than direct lying, more so when evasion is a partial-truth. This is because they do not want to deceive others nor be seen as deceptive. Receivers are sensitive to the deceptive language and more likely to act in senders’ favour when these lie directly. Our findings suggest dishonesty is more prevalent and costlier than previous best estimates focusing on direct lies.
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JEL-Classification:
C91; D82; D91;
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Discussion Paper No. 443
Getting it Right: Communication, Voting, and Collective Truth Finding
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Abstract:
We conduct an experiment in which groups are tasked with evaluating the truth of a set of politically relevant facts and statements, and we investigate whether communication improves information aggregation and the accuracy of group decisions. Our findings suggest that the effect of communication depends on the underlying accuracy of individual judgments. Communication improves accuracy when individuals tend to be incorrect, but diminishes it when individuals are likely to be correct ex ante. We also find that when groups vote independently without communicating, subjects update their beliefs in a manner consistent with interpreting others' votes as mildly informative signals, but not when they communicate beforehand. The chat analysis suggests that group members use communication to present their knowledge of related facts and to engage in interactive reasoning. Moreover, the volume of both types of communication increases with item difficulty.
Keywords:
collective decisions; voting; communication;
JEL-Classification:
D70; D72; D83;
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Discussion Paper No. 442
November 5, 2023
Fairness in Matching Markets: Experimental Evidence
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We investigate fairness preferences in matching mechanisms using a spectator design. Participants choose between the Boston mechanism or the serial dictatorship mechanism (SD) played by others. In our setup, the Boston mechanism generates justified envy, while the strategy-proof SD ensures envy-freeness. When priorities are merit-based, many spectators prefer the Boston mechanism, and this preference increases when priorities are determined by luck. At the same time, there is support for SD, but mainly when priorities are merit-based. Stated voting motives indicate that choosing SD is driven by concerns for envy-freeness rather than strategy-proofness, while support for the Boston mechanism stems from the belief that strategic choices create entitlements.
Keywords:
matching markets; school choice; voting; Boston mechanism; sincere agents; justified envy;
JEL-Classification:
D47; C92; I24; D74;
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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;