Reference Dependence and Labor Market Fluctuations
"The proposed project is concerned with Reference Dependence and Labor-Market Fluctuations (RDLMF). It aims to incorporate a fairness-based account of the labor relation into a search-and-matching (S&M) model of the labor market i...
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Información proyecto RDLMF
Líder del proyecto
TEL AVIV UNIVERSITY
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
100K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
"The proposed project is concerned with Reference Dependence and Labor-Market Fluctuations (RDLMF). It aims to incorporate a fairness-based account of the labor relation into a search-and-matching (S&M) model of the labor market in which workers' ""productivity"" fluctuates across time periods. Our analysis will explore the theoretical implications for equilibrium wage and unemployment fluctuations. We propose a framework, which as in Akerlof (1982), workers' morale (and therefore their willingness to exert discretionary, unobserved effort) is damaged when their wage falls below a reference point. Following the recent literature on reference-dependent preferences that originated with Kőszegi and Rabin (2006), we assume that the workers' reference point is a function of their lagged expectations of their labor-market outcome (specifically, their lagged-expected wage earnings). We hypothesize that the equilibrium predictions of the model are that existing workers' wages display downward rigidity with respect to macroeconomic shocks, while entry-level wages are lower and more flexible. Our model does not introduce new behavioral parameters; We hypothesize that for any given specification of the market primitives, it gives rise to higher volatility of market tightness than in the absence of reference dependence. We believe that the model is capable of producing additional insights into the way the labor market responds to macroeconomic fluctuations, insights which are difficult to address with existing models."