Thought Leadership

Estimating the hazard function of customer patience time has become a necessary component of effective operational planning such as workforce staffing and scheduling in call centers. When customers get served, their patience times are right-censored. In addition, the exact event times in call centers are sometimes unobserved and naturally binned into time intervals, due to the design of data collection systems. We develop a TunT (Transform-unTransform) estimator that turns the difficult problem of nonparametric hazard function estimation into a regression problem on binned and right-censored data. Our approach starts with binning event times and transforming event count data with a mean-matching transformation, which enables a simpler characterization of the heteroscedastic variance function. A nonparametric regression technique is then applied to the transformed data. Finally, the estimated regression function is back-transformed to yield an estimator for the original hazard function. The proposed estimation procedure is illustrated using call center data to reveal interesting customer patience behavior, and health insurance plan trial data to compare the effect between treatment and control groups. The numerical study shows that our approach yields more accurate estimates and better staffing decisions than existing methods.

Using a comprehensive sample of credit card data from a leading Chinese bank, we show that government bureaucrats receive 16% higher credit lines than non-bureaucrats with similar income and demographics, but their accounts experience a significantly higher likelihood of delinquency and debt forgiveness. Regions associated with greater credit provision to bureaucrats open more branches and receive more deposits from the local government. After staggered corruption crackdowns of provincial-level political officials, the new credit cards originated to bureaucrats in exposed regions do not enjoy a credit line premium, and bureaucrats’ delinquency and reinstatement rates are similar to those of non-bureaucrats.

We analyze optimal auction mechanisms when bidders base costly entry decisions on their valuations, and bidders pay with a fixed royalty rate plus cash. With sufficient valuation uncertainty relative to entry costs, the optimal mechanism features asymmetry so that bidders enter with strictly positive but different (ex-ante) probabilities. When bidders are ex-ante identical, higher royalty rates—which tie payments more closely to bidder valuations—increase the optimal degree of asymmetry in auction design, further raising revenues. When bidders differ ex-ante in entry costs, the seller favors the low cost entrant; whereas when bidders have different valuation distributions, the seller favors the weaker bidder if entry costs are low, but not if they are high. Higher royalty rates cause the seller to favor the weaker bidder by less, and the strong bidder by more.