Dong Huang

Why do firms outsource customer-facing activities to agents with weaker incentives to protect customer relationships? We show that delegation provides an offsetting benefit: blame shifting. We exploit a quasi-random setting at an online consumer lender that randomly assigns delinquent borrowers to in-house and third-party debt collectors, who differ systematically in collection harshness as measured by their propensity to call borrowers' social contacts. Social-contact calling raises short-run debt recovery in both sectors, but only in-house calling deters borrowers, disproportionately high-credit-quality borrowers, from returning to the lender after repaying. Outsourcing therefore preserves the recovery benefit of aggressive conduct while insulating the firm from its relationship cost.

How good is AI at persuading humans to perform costly actions? We study calls made to get delinquent consumer borrowers to repay. Regression discontinuity and a randomized experiment reveal that AI is substantially less effective than human callers. Replacing AI with humans six days into delinquency closes much of the gap. But borrowers initially contacted by AI have repaid 1% less of the initial late payment one year later and are more likely to miss subsequent payments than borrowers who were always called by humans. AI's lesser ability to extract promises that feel binding may contribute to the performance gap.

NFTs provided an extraordinary real-time laboratory for bubble economics: returns were exceptionally right-skewed, illiquidity pervaded even the most active platforms, and a handful of trades drove aggregate performance. Investors extrapolating from realized returns without recognizing selection bias and survivorship faced a substantial risk of disappointment. As our data and simulations confirm, successful NFT investing during the bubble required an almost perfect confluence of timing, liquidity, and luck.

Using transaction data from a large non-fungible token (NFT) trading platform, this paper examines how the behavioral bias of selection-neglect interacts with extrapolative beliefs, accelerating the boom and delaying the crash in the recent NFT bubble. We show that the price-volume relationship is consistent with extrapolative beliefs about increasing prices which were plausibly triggered by a macroeconomic shock. We test the hypothesis that agents prone to selection-neglect formed even more optimistic beliefs and traded more aggressively than their counterparts during the boom. When liquidity for NFTs declined, observed NFT prices were subject to severe selection bias due in part to seller loss aversion delaying the onset of the crash. Finally, we show that market participants with sophisticated bidding behavior were less subject to selection bias and performed better.