Learning strategies in modelling economic growth
Cornerstone economic growth models as the Solow-Swan model and their modern extensions normally assume the rate of population growth as exogenous without any explanation of the links between economic growth and most important demographic variables. Recently, some articles have presented models to explain many phenomena of population dynamics, including evolution and ageing. This paper is a first exercise to include endogenous population dynamics and learning strategies as ingredients of an economic growth model. The model includes two ways of learning that determinate economic growth: individual and social learning. We study the dynamics through computer simulations and we show that the model reflects some features of real economies.
Volume (Year): 31 (2011)
Issue (Month): 1 ()
|Contact details of provider:|| |
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Shigeyuki Hamori & Yu-Ching Hsieh & Wan-Jun Yao, 2007. "An Empirical Analysis about Population, Technological Progress, and Economic Growth in Taiwan," Economics Bulletin, AccessEcon, vol. 15(23), pages 1-13.
- Piero Manfredi & Luciano Fanti, 2006. "Demography In Macroeconomic Models: When Labour Supply Matters For Economic Cycles," Metroeconomica, Wiley Blackwell, vol. 57(4), pages 536-563, November.
- repec:ebl:ecbull:v:15:y:2007:i:23:p:1-13 is not listed on IDEAS
When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-10-00713. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (John P. Conley)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.