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Online Auction Demand

机译:在线拍卖需求

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With $40 billion in annual gross merchandise volume, electronic auctions comprise a substantial and growing sector of the retail economy. Using unique data on Celtic coins, we estimate a structural model of buyer and seller behavior via Markov chain Monte Carlo (MCMC) with data augmentation. Results indicate that buyer valuations are affected by item, seller, and auction characteristics; buyer costs are affected by bidding behavior; and seller costs are affected by item characteristics and the number of listings. The model enables us to compute fee elasticities even though there is no variation in fees in our data. We find that commission elasticities exceed per item fee elasticities because they target high-value sellers and enhance their likelihood of listing. By targeting commission reductions to high-value sellers, auction house revenues can be increased by 3.9%. Computing customer value, we find that attrition of the largest seller would decrease fees paid to the auction house by $97. Given the seller paid $127 in fees, competitive effects offset only 24% of those fees. In contrast, competition offsets 81% of the buyer attrition effect. In both events, the auction house would overvalue its customers by neglecting competitive effects.
机译:电子拍卖的年总商品额为400亿美元,在零售经济中占重要且不断增长的领域。使用有关凯尔特硬币的独特数据,我们通过带有数据增强功能的马尔可夫链蒙特卡洛(MCMC)估计买卖双方行为的结构模型。结果表明,买方估价受物品,卖方和拍卖特征的影响;买方成本受投标行为的影响;和卖家成本受商品特征和商品数量影响。即使数据中的费用没有变化,该模型也使我们能够计算费用弹性。我们发现佣金弹性超过了每项费用弹性,因为它们针对的是高价值卖家,并增加了上市的可能性。通过针对高价值卖家减少佣金,拍卖行的收入可以增加3.9%。通过计算客户价值,我们发现最大卖家的流失将使支付给拍卖行的费用减少97美元。考虑到卖方支付了127美元的费用,竞争性影响仅抵消了这些费用的24%。相比之下,竞争抵消了81%的买方减员效应。在这两种情况下,拍卖行都会忽略竞争效应而高估其客户。

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