Evaluating search and matching models using experimental data

This paper introduces an innovative test of search and matching models using the exogenous variation available in experimental data. We take an off-the-shelf Pissarides matching model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time limited earnings supplement for Income Assistance recipients who obtain full time employment within a 12 month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context

Evaluating Search and Matching Models Using Experimental Data

This paper introduces an innovative test of search and matching models using the exogenous variation available in experimental data. We take an off-the-shelf Pissarides matching model and calibrate it to data on the control group from a randomized social experiment. We then simulate a program group from a randomized experiment within the model. As a measure of the performance of the model, we compare the outcomes of the program groups from the model and from the randomized experiment. We illustrate our methodology using the Canadian Self-Sufficiency Project (SSP), a social experiment providing a time limited earnings supplement for Income Assistance recipients who obtain full time employment within a 12 month period. We find two features of the model are consistent with the experimental results: endogenous search intensity and exogenous job destruction. We find mixed evidence in support of the assumption of fixed hours of labor supply. Finally, we find a constant job destruction rate is not consistent with the experimental data in this context.Calibration, equilibrium search and matching models, policy experiments, Self-Sufficiency Project, welfare, social experiments

Does Observational Learning Influence Spatial Pattern Learning?

Previous studies have indicated both human and non-human animals come under control of a hidden spatial pattern when engaged in an open field search task, and rats appear to exhibit social learning in such tasks through the influence of a conspecific on their search behavior. Although human participants appear to perform similarly in both real-world and virtual environment versions of a spatial pattern search task, evidence from human participants for social learning in such a task remains lacking. The current experiments tested the influence of social learning (observational learning) on human performance in a spatial pattern learning task within a virtual environment. In Experiment 1, participants watched a video of a demonstrator performing a spatial pattern learning task using either a random search strategy (Random Observation Group) or an optimal search strategy (Optimal Observation Group). Experiment 2 tested if the obtained differences in Experiment 1 resulted from facilitation of learning in the Optimal Observation Group or inhibition of learning in the Random Observation Group by adding a third Control No Observation group. Collectively, results provide evidence for social (observational) learning by humans in a spatial pattern learning task and suggest facilitation of learning in the optimal observation group drove group differences in performance

Low Take-up Rates: The Role of Information

This paper exploits a quasi-natural experiment to study the role of information in determining take-up patterns of social benefits in a non-stigma environment. We find that take-up rate of households who have the incentive to search for information for a longer period of time is between 8 and 13 percentage points higher as compared to a control group of households. This result is robust to the inclusion of various household characteristics. Our finding provides strong empirical support for information as an important explanation for low take-up rates.take-up, social benefits, information cost

Adoption of Social Media Search Systems: An IS Success Model Perspective

The social media search system aims at providing an organized and integrated access and search support to a massive amount of unstructured, multilingual, user-generated content in an effective and efficient manner. Previous research on social media analytics mainly focuses on developing and applying advanced analysis methods and/or tools to make sense of the large amount of user-generated data over the Internet. Relatively little effort has been put to specifically examine the social media search system. In this study, we utilize and apply the DeLone and McLean IS Success Model to examine this type of systems. To do it, a lab experiment was conducted, and the results showed that all causal relationships, except for satisfaction to social benefit, specified in the DeLone and McLean IS Success Model hold in the context of the large-scale, social media search system. Specifically, we found that information quality and system quality associated with the system could significantly influence both users’ intention to use and satisfaction toward it, both of which, in turn, had significant impacts on users’ perceived individual benefit and social benefit. In addition, satisfaction could significantly influence intention to use the system. Available at: https://aisel.aisnet.org/pajais/vol10/iss2/4

Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl