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Dynamics of Firm’s Investment in Education and Training: An Agent-based Approach

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Abstract

This study developed an agent-based model of firms’ investment dynamics in education and training based on the cellular automata model. It turned the issue of the dynamics of education and training to an issue of spaces and conducted a simulation to analyze whether the strategy of firms investing in education and training will be profitable in the long term. The main results of this study can be summed up as follows: First, strategies that invest in education and training tend to be inferior to strategies that do not invest in education and training. However, even if the former strategies are inferior, a certain proportion of investing firms survive to exist in the long term. The outcome of the strategies does not largely depend on the initial proportion of education and training investment firms Second, an increase in learning speed works disadvantageously to firms that use education and training investment strategies. Third, a reduction in training cost brings a decrease in total production. Fourth, expanding the scope of information raises the productivity of a society, but acts negatively on firms that use education and training investment strategies. Fifth, an increase in the proportion of job searching due to a drop in search costs enhances the productivity of a society but works negatively on firms that use education and training investment strategies. Sixth, the government’s support for training expenses increases total production and the proportion of firms that invested in education and training as well.

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Notes

  1. Because effort expenditure was assumed to be in proportion to training costs in the function of individual workers’ effort expenditure, workers increase their effort not only in the first year when they are employed by the firm that provides training for them but also continually in the following years.

  2. In the following explanations, industry index i is omitted unless it has important meanings.

  3. The results of this study are not sensitive to the specific form of the production function. In the author's other work, Yang (2018)-written in Korean, we assumed and analyzed specific forms of production functions rather than the Sugarscape model, and the main results were substantially the same.

  4. You may request the program code to the researcher in charge by email at jseyang@krivet.re.kr.

  5. Someone may suspect this as the error of the learning method of this study. In other words, the simulation of strategies allocated at random in the initial period may have been maintained to the end as it was. However, it was found that when changes in the sensitivity strategy of workers’ effort expenditure were tracked, the strategy of individual workers has continually changed.

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Acknowledgements

This study was funded by the Korea Research Institute for Vocational Education & Training(KRIVET).

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  1. Social Policy Building, Sejong National Research Complex, Sicheong-daero 370, Sejong, 30147, South Korea

    Jung-Seung Yang

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Yang, JS. Dynamics of Firm’s Investment in Education and Training: An Agent-based Approach. Comput Econ 60, 1317–1351 (2022). https://doi.org/10.1007/s10614-021-10206-6

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