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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31411
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| Title: | Hiring and Learning in Online Global Labor Markets |
| Authors: | Mill, Roy |
| Keywords: | International outsourcing; Online labor market; Information acquisition;
Quality reputations; Country-of-origin effect; Statistical discrimination |
| Issue Date: | 21-Dec-2011 |
| Series/Report no.: | NET Institute Working Papers;11_17 |
| Abstract: | This paper uses data from freelancer.com – an online platform that
allows employers and freelancers to search for, and match with, each
other – to study the effect of freelancers’ country of
origin on their likelihood to be hired. Having to rely on a relatively
small number of characteristics, employers use the freelancer’s
country of origin to infer the expected service’s quality. This
setting also allows me to document how employers’ experience in
past hires affects their behavior in current hires. I find that
freelancers from developing countries are less likely to be hired when
they have no individual reputation, and as individual reputation becomes
better this country effect disappears. I show that following a good
match with a freelancer, employers are more likely to hire freelancers
from the good match’s country. These these findings are consistent
with statistical – rather than purely taste-based – discrimination. |
| URI: | http://hdl.handle.net/2451/31411 |
| Appears in Collections: | NET Institute Working Papers Series
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Files in This Item:
| File |
Description |
Size | Format |
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| 11_17.pdf | NET Institute Working Paper 11_17 | 1.77 MB | Adobe PDF | View/Open |
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