Information acquisition in an all-pay auction contest
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Date
2023
Journal Title
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Abstract
We develop a two-player all-pay auction model where an incumbent competes with an entrant. The incumbent’s valuation is publicly known, whereas the entrant’s valuation is unknown but has a publicly known prior distribution. However, before competing in the all-pay auction, the entrant can acquire additional information about his valuation through a learning experiment. This experiment enables him to learn his
valuation perfectly, but we also allow for intermediate levels of information. We find that the entrant maximizes his expected payoff by perfectly learning his valuation. Furthermore, the entrant has a greater willingness to pay for information when competing against an incumbent with a similar ex-ante valuation, and a lesser willingness to pay for information when competing against a stronger or weaker incumbent. Nonetheless, the entrant is less willing to pay for information when he competes with a weaker incumbent than a stronger one. We then test the robustness of these results and find that the entrant’s perfect learning outcome is robust only for certain incumbent valuations.
Description
Tesis (Magíster en Economía)--Pontificia Universidad Católica de Chile, 2023
Keywords
Contests, All-pay auction, Information acquisition, Competition