Social learning and parameter uncertainty in irreversible investments: Evidence from greenhouse adoption in northern China

This paper aims at an important gap in the literature, which has not modeled the effect of social learning in a real option context and examined uncertainty-reduction measures through social learning. This paper addresses the gap by modeling social learning as a way of reducing parameter uncertainty, thus facilitating technology adoption and shortening the waiting time in irreversible investments. We use household-level data on intermediate-technology greenhouse adoption in northern China to test the predictions in both a linear probability model and a duration analysis. Our empirical findings support the hypothesis. We also find that market volatility and insecure land property rights discourage adoption.