Date of Award
8-2025
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Economics and Finance
Committee Chair(s)
Lucas Rentschler (Committee Chair)
Committee
Lucas Rentschler
Committee
Sherzod B. Akhundjanov
Committee
Katarzyna Anna Bilicka
Committee
T. Scott Findley
Abstract
This paper investigates whether the strategic bargaining models developed in game theory align with outcomes observed in laboratory and field settings. We aim to synthesize theoretical literature with experimental and field data to assess the predictive power of game-theoretic models in labor-union wage negotiation contexts. We analyze Rubinstein's bargaining model and its extensions, focusing on how relaxed assumptions affect equilibrium behavior, gains from trade, and dispute occurrence. The theoretical analysis reveals that many bargaining failures stem from asymmetric information between both parties, where delay, transaction costs, deadlines, and reputational effects lead to distinct equilibrium predictions. To test these theoretical predictions, we examine a broad array of ultimatum-style and structured bargaining experiments to identify alignment with the theoretical framework. Our experimental analysis shows that while simple ultimatum games often deviate from theoretical predictions due to fairness concerns, modified experiments with appropriate controls (such as discount factors, anonymity, and learning mechanisms) align closely with theory. Laboratory studies on disagreement payoffs and reputational concerns consistently support theoretical frameworks, though behavioral biases can still persist. Field data alignment proves more challenging. Most empirical union-firm studies focus on wage premiums rather than bargaining dynamics, making direct theoretical mapping difficult. However, structural estimation approaches and emerging real-time data analysis using natural language processing show promise.
Recommended Citation
Hasnat, Sajid Bin, "Do the Theoretical Frameworks Surrounding Union Wage Negotiations Align With Empirical Data?" (2025). All Graduate Reports and Creative Projects, Fall 2023 to Present. 108.
https://digitalcommons.usu.edu/gradreports2023/108
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