Author
Abstract
This paper investigates the long-run effects of immigration on wages and welfare in a model with endogenous technology choice (ETC) where firms are allowed to choose their optimal skill intensity from a menu of available technologies. I embed the ETC framework into the Auerbach and Kotlikoff model (1987) that features a large set of overlapping generations, a rich collection of population dynamics, and a social security system. I calibrate the model to match with the U.S. data and evaluate the effect of ETC with the help of two experiments. In the first experiment, I increase the share of high-skilled immigrants and compare the wage and welfare predictions of the model with ETC to a standard model where the skill intensities in production technology are fixed. In the standard model, since the skill intensities are constant, increase in the supply of high-skilled labor leads to a decrease in high-skilled wages and an increase in low-skilled wages. On the other hand, in the model with ETC, negative supply-side effects are counterbalanced by an increase in the intensity of the more abundant high-skilled labor, leading to a smaller decrease in their wages. The discrepancy between wage predictions of these two models is also reflected in the welfare: while the model with ETC predicts an increase in both high- and low-skilled natives’ welfare, the standard model would predict a decrease in the welfare of the high skilled and a larger increase in the welfare of the low skilled. In the second experiment, I examine the effects of an increase in low-skilled immigration and find that in this case, since the initial production technology is low-skilled intensive, the ETC effects are smaller. These results imply that if ETC is ignored, both in the short run and long run, wage and welfare analyses of immigration will be incomplete, and even misleading.
Suggested Citation
Senel, Gonca, 2022.
"Immigration, Endogenous Skill Bias Of Technological Change, And Welfare Analysis,"
Macroeconomic Dynamics, Cambridge University Press, vol. 26(5), pages 1264-1301, July.
Handle:
RePEc:cup:macdyn:v:26:y:2022:i:5:p:1264-1301_5
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:macdyn:v:26:y:2022:i:5:p:1264-1301_5. See general information about how to correct material in RePEc.
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.
We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/mdy .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.