Endogenous Separations, Wage Rigidities, and Unemployment Volatility

We show that in microdata, as well as in a search and matching model with flexible wages for new hires, wage rigidities of incumbent workers have substantial effects on separations and unemployment volatility. Allowing for an empirically relevant degree of wage rigidities for incumbent workers drives unemployment volatility as well as the volatility of vacancies and tightness to that in the data. Thus, the degree of wage rigidity for newly hired workers is not a sufficient statistic for determining the effect of wage rigidities on macroeconomic outcomes. This finding affects the interpretation of a large empirical literature on wage rigidities. (JEL E24, J23, J31, J41, J63)

How Sticky Wages in Existing Jobs Can Affect Hiring

We consider a matching model of employment with flexible wages for new hires but sticky wages within matches. Unlike most models of sticky wages, we allow effort to respond if wages are too high or too low. In the Mortensen-Pissarides model, employment is not affected by wage stickiness in existing matches. But it is in our model. If wages of matched workers are stuck too high, firms require more effort, lowering the value of additional labor and reducing hiring. We find that effort’s response can greatly increase wage inertia. (JEL E24, J23, J31, J41, M51)

A two-sided gaming model for large-scale stable matching in sharing economy based on the probabilistic linguistic term sets

Sharing economy is significant for economic development, stable matching plays an essential role in sharing economy, but the large-scale sharing platform increases the difficulties of stable matching. We proposed a two-sided gaming model based on probabilistic linguistic term sets to address the problem. Firstly, in previous studies, the mutual assessment is used to obtain the preferences of individuals in large-scale matching, but the procedure is time-consuming. We use probabilistic linguistic term sets to present the preferences based on the historical data instead of time-consuming assessment. Then, to generate the satisfaction based on the preference, we regard the similarity between the expected preferences and actual preferences as the satisfaction. Considering the distribution features of probabilistic linguistic term sets, we design a shape-distance-based method to measure the similarity. After that, the previous studies aimed to maximize the total satisfaction in matching, but the individuals’ requirements are neglected, resulting in a weak matching result. We establish the two-sided gaming matching model from the perspectives of individuals based on the game theory. Meanwhile, we also study the competition from other platforms. Meanwhile, considering the importance of the high total satisfaction, we balance the total satisfaction and the personal requirements in the matching model. We also prove the solution of the matching model is the equilibrium solution. Finally, to verify the study, we use the experiment to illustrate the advantages of our study.

Are Drug-Free School Zones Effective? Evidence From Matching Schools and School-like Entities

Using a combination of spatial and statistical analysis, this paper focuses on analyzing the effectiveness of drug-free school zones (DFSZ) around K-12 schools in Los Angeles County. A propensity score matching model is employed to match schools and school-like entities to compare the amount of drug crimes in two distinct 1000-foot buffers surrounding them. The model is then compared to a coarsened exact matching model. The average treatment effects (ATE) and average treatment effects on the treated (ATT) are estimated. Our results indicate that there are 2.7 and 1.7 fewer drug crimes and non–marijuana-related drug crimes respectively near schools, as a result of the policy. The total effect of the policy is estimated to reduce drug crime near schools by between 1065 to 1643 fewer incidences per year. Furthermore, we find no significant differences in gang-related drug crimes, gang-related violent crimes, or property crimes as a result of the policy.

Two-Sided Matching Model of Shared Manufacturing Resources considering Psychological Behavior of Agents

Sharing manufacturing is a new manufacturing mode based on sharing economy, which is one of the pillars of intelligent manufacturing. This paper proposes a two-sided matching model of shared manufacturing resources considering the psychological behavior of agents. We describe the definition of two-sided matching and introduce the concept of the cloud model. The preference information of agents is transformed to values according to the cloud model. We combine prospect theory and grey relational analysis to calculate the prospect values. Furthermore, an optimization model which aims to maximize the overall satisfaction degree of matching agents is established. A numerical example for the matching of providers and demanders is provided to verify the feasibility and effectiveness of the model.