Aarhus Finance Forum 2026
August 2 to 4, 2026 at Aarhus University in Aarhus, Denmark
Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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CF 2: Corporate Finance II: Labor
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The Fragile Promise: Job Security and Contract Design under Partial Commitment 1The Chinese University of Hong Kong, Shenzhen; 2The University of Amsterdam; 3University College London We develop a dynamic contracting framework to study how partial commitment power affects long-term employment relationships. In a continuous-time moral hazard model, the principal can alter the contract at random, exogenously timed alteration opportunities, with the arrival rate measuring her (lack of) commitment power. Our results depend on the relative observability of diligence and misconduct. For guardian jobs, where misconduct is easier to observe, it is optimal to offer a static, permanent contract that pays fixed performance bonuses for observed diligence and remains immune to reductions in commitment power. For entrepreneurial jobs, where diligence is easier to observe, the optimal structure features an initial probation period, a promotion bonus upon achieving permanence, and a return to probation at each alteration opportunity. In this case, weaker commitment can, under certain conditions, improve welfare; if commitment power is already low, further reductions may yield Pareto improvements. Reduced commitment power also raises required promotion and performance bonuses, shortens probation, and accelerates the decline in promotion bonuses over time. Does the Peter Principle Apply to Paula? Incentive Responsiveness and Promotion Decisions in Financial Institutions Southern Methodist University, United States of America Firms face a costly trade-off in promotion decisions: reward top performers to motivate effort or select those with the highest expected managerial performance. We test whether firms mitigate these costs by placing greater weight on managerial fit for workers who respond less strongly to promotion incentives. Prior research suggests women are more likely to exhibit traits such as greater risk aversion and lower competitiveness, which moral hazard models predict dampen effort responses to promotion-based incentives. Exploiting mandatory disclosure requirements in the U.S. mortgage industry, we analyze promotions among 88,000 loan officers at approximately 1,000 firms using Becker’s (1993) outcome test. For male workers, we find that firms’ promotion decisions favor current sales performance over future managerial performance, consistent with the Peter Principle. In contrast, firms place greater weight on managerial fit for female workers, mitigating the Peter Principle mismatch costs. We also provide direct evidence on the mechanism that promotion incentives are more consequential for men’s behavior than for women’s: female loan officers are less negatively affected than male loan officers when promotion incentives weaken after being passed over, respond less positively when incentives strengthen because a managerial position becomes available, and are less likely to exit the industry following high performance. Our results show that firms mitigate the incentive–fit conflict in promotions, confirming the predictions of economic theory. Successfully Fired: The Unique Incentives of Agentic-AI Adoption UT-Dallas, United States of America We study optimal incentive contracts when workers privately observe whether Agentic AI can automate their jobs. Firms balance bonuses for truthful reports of successful automation with termination threats. Workers may be fired regardless of automation success (mass termination), even though dismissing non-automatable workers destroys value. Mass termination becomes more likely when automation probability rises or workers capture more surplus. Firm value is convex in automation probability, while worker utility is non-monotonic, rationalizing divergent attitudes toward Agentic AI. Mass layoffs increase in frequency over time without improvements in automation, and are less likely in firms employing many similar workers. | ||
