Non-Employment Benefits and the Evolution of Worker-Employer
Cooperation:
Experiments with Real and Computational Agents
Mark Pingle, Leigh Tesfatsion
tesfatsi@iastate.edu
Paper URL: http://www.econ.iastate.edu/tesfatsi/sce2001.pdf
It is now commonly understood that the complexity
of most employment relationships forces the typical employment contract to
be incomplete. If the contract does not enforce the desired level of
cooperation, it is reasonable to think that other institutions might arise
to do the job. Using experiments with both real and computational agents,
this paper examines the possibility that the level of non-employment benefits
affects the level of cooperation between workers and employers, thereby impacting
the unemployment rate, the productivity of labor, and a variety of other economic
outcomes.
A distinctive feature of our experimental employment
study relative to previous theoretical studies is that matches between workers
and employers are determined endogenously, on the basis of past worksite experiences,
rather than randomly in accordance with some exogenously specified probability
distribution. In each stage, workers either direct work offers to preferred
employers or choose unemployment and receive the non-employment payoff, and
employers either accept work offers from preferred workers (subject to capacity
limitations) or remain vacant and receive the non-employment payoff.
Matched workers and employers participate in a risky employment relationship
modeled as a prisoner's dilemma game. Both the computational agents
and the human agents evolve their partner preferences and worksite behaviors
over time on the basis of past matching and worksite experiences.
In both types of experiments, increases in the
non-employment payoff result in higher average unemployment and vacancy rates
while at the same time encouraging cooperation among the worker and employers
who do form matches. On the other hand, given a high non-employment
payoff, an increasing number of the computational workers and employers learn
over time to coordinate on mutual cooperation and avoid coordination failure,
so that overall efficiency increases as well. This potentially important``longer
run'' policy effect is not clearly evident in the necessarily shorter trials
run with human subjects. This difference raises challenging issues both
for human-subject experimentalists wishing to conduct social policy impact
studies and for computational experimentalists who wish to use human-subject
experiments to validate their computational findings.