Discussion Paper No. 558
December 22, 2025
Delegating in the Age of AI: Preferences for Decision Autonomy
Author:
Abstract:
Despite the documented benefits of algorithmic decision-making, individuals often prefer to retain control rather than delegate decisions to AI agents. To what extent are the aversion to and distrust of algorithms rooted in a fundamental discomfort with giving up decision authority? Using two incentivized laboratory experiments across distinct decision domains, hiring (social decision-making) and forecasting (analytical decision-making), and decision architecture (nature and number of decisions), we elicit participants’ willingness to delegate decisions separately to an AI agent and a human agent. This within-subject design enables a direct comparison of delegation preferences across different agent types. We find that participants consistently underutilize both agents, even when informed of the agents’ superior performance. However, participants are more willing to delegate to the AI agent than to the human agent. Our results suggest that algorithm aversion may be driven less by distrust in AI and more by a general preference for decision autonomy. This implies that efforts to increase algorithm adoption should address broader concerns about control, rather than focusing solely on trust-building interventions.
Keywords:
algorithm; delegation; artificial intelligence; trust in ai; experiment; preferences;
JEL-Classification:
C72; C91; D44; D83;
Download:
Discussion Paper No. 557
AI Tutoring Enhances Student Learning Without Crowding Out Reading Effort
Author:
Abstract:
We study how AI tutoring affects learning in higher education through a randomized experiment with 334 university students preparing for an incentivized exam. Students either received only textbook material, restricted access to an AI tutor requiring initial independent reading, or unrestricted access throughout the study period. AI tutor access raises test performance by 0.23 standard deviations relative to control. Surprisingly, unrestricted access significantly outperforms restricted access by 0.21 standard deviations, contradicting concerns about premature AI reliance. Behavioral analysis reveals that unrestricted access fosters gradual integration of AI support, while restricted access induces intensive bursts of prompting that disrupt learning flow. Benefits are heterogeneous: AI tutors prove most effective for students with lower baseline knowledge and stronger self-regulation skills, suggesting that seamless AI integration enhances learning when students can strategically combine independent study with targeted support.
Keywords:
ai tutors; large language models; self-regulated learning; higher education;
JEL-Classification:
C91; I21; D83;
Download:
Discussion Paper No. 556
Visual and Social Anchoring in a Framed Online Rating Experiment
Author:
Abstract:
We conduct an online experiment to assess the effect of the anchoring bias on consumer ratings. We depart from the canonical anchoring literature by implementing non-numerical (visual) anchors in a framed rating task. We compare three anchoring conditions, with either high, low, or socially derived anchors present, against two control conditions – one without anchors and one without framing. Our framing replicates the common observation of overrating. We unveil asymmetric non-numerical anchoring effects that contribute to the explanation of overrating. Both high anchors and socially derived anchors lead to significant overrating compared to the control condition without anchors. The latter finding is driven by instances of high social anchors. The upward rating bias is exacerbated in a social context, where participants exhibit more trust in anchors. In contrast, low anchors and instances of low social anchors have no effect compared to the control condition without anchors. Beyond consumer ratings, our results may have broader implications for online judgment environments, such as surveys, crowdfunding platforms, and other user interfaces that employ visual indicators such as stars, bars, or progress displays.
Keywords:
anchoring bias; consumer judgment; economic experiment; online feedback systems; user interface design;
JEL-Classification:
C91; D80; D91;
Download:
Discussion Paper No. 555
December 1, 2025
Decreasing Returns to Sampling Without Replacement
Author:
Abstract:
We study sampling from a finite population without replacement when seeking an extreme (lowest or highest) value. An example is a buyer searching for the lowest price. It is well known that there are decreasing returns to sampling from continuous populations: the expected minimum is a decreasing and discretely convex function of the sample size. We show that is true for sampling without replacement from a finite population. We also give a simple sufficient condition on population values for the properties to hold for other order statistics.
Keywords:
order statistics; sampling without replacement; decreasing returns; consumer search;
JEL-Classification:
Download:
Discussion Paper No. 554
November 25, 2025
Oligopolistic Information Markets
Author:
Abstract:
In modern information markets, buyers routinely combine signals from multiple sellers. We develop a model of ``portfolio competition'' to analyze this distinctive feature. We show that the combinability of information overturns standard oligopoly intuition. Unlike traditional markets, competitive pressure does not necessarily protect buyers: when signals are complements, sellers can leverage the buyer's desire for the joint portfolio to extract the full social surplus, regardless of the number of competitors. We characterize the precise conditions for rent extraction, which reduce to a simple geometric test for symmetric sellers. Furthermore, we find that the canonical logic of market entry fails. Entry is never socially excessive because efficient portfolio choices eliminate business-stealing effects. Paradoxically, entry can reduce competitive pressure: when entrants provide strong complementarities, they shift the buyer's threat point, allowing all sellers to extract higher rents.
Keywords:
information markets; portfolio competition; market entry; data economy; complementarity;
JEL-Classification:
Download:
Discussion Paper No. 553
Demand-Investment in Distribution Channels
Author:
Abstract:
We study a manufacturer's demand-investment decisions in distribution channels subject to double marginalization. Casting this as a mechanism design problem, we show that demand-enhancing investments strengthen retailers' incentives to exploit market power, forcing manufacturers to concede greater rents. Manufacturers therefore optimally restrict product quality or market coverage. We fully characterize which demand parameters create these perverse incentives: increases benefit manufacturers in segments where they control pricing but harm them in segments with binding incentive constraints. This reveals fundamental limits to demand-side investment in vertical relationships.
Keywords:
demand; investment incentives; distribution channels; double marginalization;
JEL-Classification:
D21; D82; L11;
Download:
Discussion Paper No. 552
November 20, 2025
The Elusive Returns to AI Skills: Evidence from a Field Experiment
Author:
Abstract:
As firms increasingly adopt Artificial Intelligence (AI) technologies, how they adjust hiring practices for skilled workers remains unclear. This paper investigates whether AI-related skills are rewarded in talent recruitment by conducting a large-scale correspondence study in the United Kingdom. We submit 1,185 résumés to vacancies across a range of occupations, randomly assigning the presence or absence of advanced AI-related qualifications. These AI qualifications are added to résumés as voluntary signals and not explicitly requested in the job postings. We find no statistically significant effect of listing AI qualifications in résumés on interview callback rates. However, a heterogeneity analysis reveals some positive and significant effects for positions in Engineering and Marketing. These results are robust to controlling for the total number of skills listed in job ads, the degree of match between résumés and job descriptions, and the level of expertise required. In an exploratory analysis, we find stronger employer responses to AI-related skills in industries with lower exposure to AI technologies. These findings suggest that the labor market valuation of AI-related qualifications is context-dependent and shaped by sectoral innovation dynamics.
Keywords:
return to skills; technological change; labor market; hiring; signaling; human capital; field experiment; ai-related skills;
JEL-Classification:
O33; J23; J24; I26;
Download:
Discussion Paper No. 551
Training or Retiring? How Labor Markets Adjust to Trade and Technology Shocks
Author:
Abstract:
How do firms and workers adjust to trade and technology shocks? We analyze two mechanisms that have received little attention: training that upgrades skills and early retirement that shifts adjustment costs to public pension systems. We combine novel data on training participation and early retirement in German local labor markets with established measures of exposure to trade competition and robot adoption. Results indicate that negative trade shocks reduce training—particularly in manufacturing—while robot exposure increases training—particularly in indirectly affected services. Both shocks raise early retirement among manufacturing workers. Structural change thus induces both productivity-enhancing and productivity-reducing responses, challenging simple narratives of labor market adaptation and highlighting the scope for policy to promote adjustment mechanisms conducive to aggregate productivity.
Keywords:
training; retirement; trade; technological change; automation; robots; firms; workers; labor market;
JEL-Classification:
J24; J26; O33; F16; R11;
Download:
Discussion Paper No. 550
Rising Inequality, Declining Mobility: The Evolution of Intergenerational Mobility in Germany
Author:
Abstract:
This paper is the first to show that intergenerational income mobility in Germany has decreased over time. We estimate intergenerational persistence for the birth cohorts 1968-1987 and find that it rises sharply for cohorts born in the late 1970s and early 1980s, after which it stabilizes at a higher level. As a step towards understanding the mechanisms behind this increase, we show that parental income has become more important for educational outcomes of children. Moreover, we show that the increase in intergenerational persistence coincided with a surge in cross-sectional income inequality, providing novel evidence for an "Intertemporal Great Gatsby Curve''.
Keywords:
intergenerational mobility; social mobility; income; education; inequality;
JEL-Classification:
J62; I24; D63;
Download:
Discussion Paper No. 549
Rare Disasters, Tail Aversion, and Asset Pricing Puzzles
Author:
Abstract:
This paper integrates tail aversion, implemented via a one-period entropic tilt, with rare disasters in a consumption-based asset pricing model with CRRA utility to jointly address the equity premium and risk-free rate puzzles. The model delivers closed-form expressions for the risk-free rate and asset moments, pushes out the Hansen-Jagannathan bound, implies a low risk-free rate via diffusion and disaster channels, and delivers natural upper and lower bounds of risk aversion. Calibrated to long-run return data and disciplined by disaster evidence, the model matches average returns, volatility, and a low real risk-free rate with very modest risk aversion.
Keywords:
equity premium puzzle; risk-free rate puzzle; rare disasters; entropic tilt; multiplier (kl) preferences; robust control; consumption-based asset pricing;
JEL-Classification:
G12; E44; E43; E21; D81;