Journal Article
No. 2016-29 | November 07, 2016
Job Placement Agencies in an Artificial Labor Market


In this paper, an agent-based search and matching (ABSAM) model of a local labor market with heterogeneous agents and an on-the-job search is developed, i.e. job seekers who vary in unemployment duration, skills levels and preferences compete for vacancies which differ for skills demands and the sector of the economy. Job placement agencies help unemployed persons find appropriate job vacancies by improving their search effectiveness and by sharing job advertisements. These agents cooperate in an artificial labor market where the key economic conditions are imposed. The interactions between the participants are drawn directly from labor market search theory. The main research task was to measure the direct and indirect impacts of labor market policies on labor market outcomes. The global parameters of the ABSAM model were calibrated with the Latin hypercube sampling technique for one of the largest urban areas in Poland. To study the impact of parameters on model output, two global sensitivity analysis methods were used, i.e. Morris screening and Sobol indices. The results show that the job placement agencies’ services, as well as minimum wage and unemployment benefits, considerably interact with and influence unemployment and long-term unemployment ratios, wage levels, duration of periods of unemployment, skills demand, and worker turnover. Moreover, strong indirect effects were detected, e.g. programs aimed at one group of job seekers affected other job seekers and the whole economy. This impacts are sometimes positive and sometimes negative.

Data Set

JEL Classification:

C63, C69, J48, J63, J64


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Cite As

Marcin Wozniak (2016). Job Placement Agencies in an Artificial Labor Market. Economics: The Open-Access, Open-Assessment E-Journal, 10 (2016-29): 1–53.

Comments and Questions

November 10, 2016 - 10:02

I commend the author for this innovative paper. The theory for me sounds good except for the obvious missing gender perspective that would make this model even more innovative. As we know men and women do not have the same transition experiences from unemployment to paid work nor from inactivity ...[more]

... to paid work. In any case I really like the spatial matching algorithm since it can be adapted to allow for the much higher constraint women face in looking for jobs that are closer to their residences given their almost exclusive tasks of care in the households. The time use surveys are really big eye opener in this sense, there is definitely a spatial constraint in the case of women. There would also have to be some thought int he modle on the effect on wages as far as the persistent differences between women and men. As an anecdote, would like to share the alarm by a mayor of a town in the Madrid region when one of our conclusions of a large socio economic study of the place was that registered unemployment would rise substantially once the new public employment service office was opened, specially among women and youth (so far unemployed persons from this town had to travel to the next town over to register). And so it did happen… Will be very itneresting to start testing this model with actual data.

Marcin Wozniak - Reply to the comment
November 10, 2016 - 11:55

Thank you very much for your comment. I agree with you that the agents in the model could be gender specified which could bring us some valuable feedback. The main question in this context is how to calculate the value functions in the case of representative man and woman agent. ...[more]

... Would the transition probabilities be the only differences? Probably not.

We could also make some further distribution. What about gender, age, race and religion differences at the same time? The model complexity rises in this case, however, I believe it could be still implemented.

November 11, 2016 - 07:43

Indeed, but what we do have for now are time use surveys that can help model these differnet spatial patterns. Again congratulations on the paper.