We study infant industry protection using a dynamic model in which the industry's cost is initially higher than that of foreign competitors. The industry can stochastically lower its cost via learning by doing. Whether the industry has transitioned to low cost is private information. Using a mechanism-design approach, we solve for the optimal protection policy that induces the industry to reveal its true cost. We show that (i) the optimal protection, measured by infant industry output, declines over time and is less than that under public information, (ii) the optimal protection policy is time consistent under public information but not under private information, (iii) eventual viability of the infant industry is neither necessary nor sufficient for the optimal protection under private information, and (iv) the optimal protection policy can be implemented with minimal information requirements. We also deliver a one-dimensional metric to rank industries to help a government with a limited budget to choose which industries to protect.
https://doi.org/10.20955/wp.2022.013
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