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Learning Through Referrals

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  • Manolis Galenianos

    (Pennsylvania State University)

Abstract

This paper theoretically examines the firm's choice to use different search channels in order to hire new workers. An equilibrium model is developed where the quality of a match is uncertain and firms search for workers through the market and through referrals. The intensity of use of each search channel is endogenized through the choice of channel-specific search effort. When referrals generate more accurate signals regarding match quality, the model predicts that referred workers have higher starting wages, higher productivity and lower separation rates than non-referred candidates and that these differentials decrease over time due to selection, which is consistent with the data. The model is extended by introducing productivity heterogeneity in firms and allowing the endogenous determination of signal quality. It is shown that high productivity firms choose greater accuracy of signals which diminishes the referral-market differential and leads to lower referral intensity, consistent with the data. This type of selection on the firm side explains why regressions that do not include firm fixed effects find a negative effect of referrals on wages in contrast to firm-level and other evidence.

Suggested Citation

  • Manolis Galenianos, 2012. "Learning Through Referrals," 2012 Meeting Papers 814, Society for Economic Dynamics.
    • Handle: RePEc:red:sed012:814
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    References listed on IDEAS

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