An evolutionary model of firms' institutional behavior focusing on labor decisions

The understanding of the economy's aggregate growth patterns is a fundamental objective of economic growth theorizing. However, the micro constructions are strongly linked to economic growth and so cannot be neglected in such process. This paper is concerned with this problem, proposing a formal mechanism to establish the bridge between macro regularities and micro evolutionary behavior. Within a micro to macro or bottom-up perspective, the adopted approach is focused in the influence of firms’ ‘institutional settings’ on economic growth and in the industry dynamics that lies behind more aggregate behaviors. The analysis associates such settings to firms’ labor choices in terms of hiring/firing policies and to their screening capabilities. Building a computer simulation model which deals with the nature and evolution of the knowledge that guides firms’ efforts to improve their institutional settings, we were able to draw some important implications. The results show that firm’s ability to change its ‘institutional setting’ is crucial for its survival. In a model without a learning mechanism the results show significant turbulence in terms of exit and entry of firms and no significant connection with the firm’s ‘institutional set’. In the LearnModel, the outcome is much more stable, with initial firms surviving for long period of time. Results also suggest that the presence of a learning mechanism is particularly striking in what concerns firms’ behavior and industry’s dynamics. The survival probability depends on firms’ hiring efficiency and on their ability to react to environmental changes. Since firms’ hiring efficiency and their learning rates depend on their accumulated non-routine workers, the results seem to imply some ‘lock-in’ paths. Firms with initial low values of relative non-routine workers have lower chances of survival. However, firms with initial high values of relative non-routine workers will survive if and only if they rapidly improve their hiring efficiency.