Abstract
This paper presents an agent-based model of labor market participation, in which a population of agents is affected by adverse health shocks that impact the costs associated with work efforts, and decides whether to leave the labor market. This decision is simply taken by looking at the working behaviors of the other agents, comparing the respective levels of well-being and imitating the more advantageous decision of others. The analysis reveals that such mechanism of social learning suffices to replicate the existing empirical evidence regarding the decline in labor market participation of older people. As a consequence, the paper demonstrates that it is not necessary to assume perfect and unrealistic rationality at the individual level to reproduce a rational behavior in the aggregate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
This is an Herculean task requiring, among other things, to estimate transition probabilities from one state to all possible future states based on beliefs which, in turn, depends on 26 parameters to be determined. Other questionable assumptions include “rational expectations”, a notion according to which all individual subjective probabilities equate the “objectively estimable population probability” and the capability to anticipate regulatory changes. The paper acknowledges several times that only some justification for such assumptions can be provided.
- 2.
These statistics include people both in and outside the labor force. The data used to draw the curves in Fig. 1 are taken from the Table 4 A.3 in the Annex 4.1 - Tables on Work and Retirement of the first wave of the ELSA survey. http://www.elsa-project.ac.uk/reportWave1.
- 3.
As mentioned previously, pensioners and pension income can also be interpreted as people who decide to leave the labor market and receive a subsidy or a disability benefit.
- 4.
Simulations are performed using the BehaviorSpace tool in NetLogo, which allows to specify the grid of parameter values and the number of simulations for each point of the grid.
- 5.
These seven classes of age correspond to the ones presented in the Table 4 A.3 in the Annex 4.1 - Tables on Work and Retirement of the first wave of the ELSA survey.
References
Axtell, R.L., Epstein, J.M.: Coordination in transient social networks: an agent-based computational model of the timing of retirement. In: Aaron, H.J. (ed.) Behavioral Dimensions of Retirement Economics, pp. 161–183. Brookings Institution Press, Washington, DC (1999)
Bound, J., Stinebrickner, T.R., Waidmann, T.A.: Health, economic resources and the work decisions of older men. J. Econometrics 156, 106–129 (2010)
Burkhauser, R.V., Butler, J.S., Gumus, G.: Dynamic programming model estimates of social security disability insurance application timing. J. Appl. Econometrics 19, 671–685 (2004)
Camerer, C.F.: Progress in behavioral game theory. J. Econ. Perspect. 11, 167–188 (1997)
Epstein, J.M.: Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science. Princeton University Press, Princeton (2014)
French, E.: The effects of health, wealth, and wages on labour supply and retirement behaviour. Rev. Econ. Stud. 72, 395–427 (2005)
Gilleskie, D.B.: A dynamic stochastic model of medical care use and work absence. Econometrica 66, 1–45 (1998)
Goudet, O., Kant, J.D., Ballot, G.: Forbidding fixed duration contracts: unfolding the opposing consequences with a multi-agent model of the french labor market. In: Amblard, F., Miguel, F.J., Blanchet, A., Gaudou, B. (eds.) Advances in Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol. 676, pp. 151–167. Springer, Switzerland (2015)
Gustman, A.L., Steinmeier, T.L.: Retirement and the stock market bubble. NBER Working Paper No. 9404, National Bureau of Economic Research, Cambridge (2002)
Heyma, A.: A structural dynamic analysis of retirement behaviour in the Netherlands. J. Appl. Econometrics 19, 739–759 (2004)
Lettau, M.: Explaining the facts with adaptive agents: the case of mutual fund flows. J. Econ. Dyn. Control 21, 1117–1147 (1997)
Lurie, N.H.: Decision making in information-rich environments: the role of information structure. J. Consum. Res. 30, 473–486 (2004)
Malhotra, N.K.: Information load and consumer decision making. J. Consum. Res. 8, 419–430 (1982)
OECD: How’s Life? 2013: Measuring Well-being. OECD Publishing, Paris (2013)
Rabin, M.: Psychology and economics. J. Econ. Lit. 36, 11–46 (1998)
Rust, J., Phelan, C.: How social security and medicare affect retirement behavior in a world of incomplete markets. Econometrica 65, 781–831 (1997)
Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston (1999). http://ccl.northwestern.edu/netlogo/
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Moro, A., Pellizzari, P. (2016). An Agent-Based Model of Labor Market Participation with Health Shocks. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M. (eds) Advances in Practical Applications of Scalable Multi-agent Systems. The PAAMS Collection. PAAMS 2016. Lecture Notes in Computer Science(), vol 9662. Springer, Cham. https://doi.org/10.1007/978-3-319-39324-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-39324-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39323-0
Online ISBN: 978-3-319-39324-7
eBook Packages: Computer ScienceComputer Science (R0)