Latest Research Publications

Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem. We directly model the relationship between the conditional distribution of the random parameter and the contextual features and then estimate the probabilistic model with an objective that aligns with the downstream optimization problem. We show that our ICEO approach is asymptotically consistent under moderate regularity conditions and further provide finite performance guarantees in the form of generalization bounds. Computationally, performing estimation with the ICEO approach is a nonconvex and often nondifferentiable optimization problem. We propose a general methodology for approximating the potentially nondifferentiable mapping from estimated conditional distribution to the optimal decision by a differentiable function, which greatly improves the performance of gradient-based algorithms applied to the nonconvex problem. We also provide a polynomial optimization solution approach in the semi-algebraic case. Numerical experiments are also conducted to show the empirical success of our approach in different situations, including with limited data samples and model mismatches.

Request-for-quote (RFQ) is the most commonly used mechanism for contractor selection in project outsourcing. Problem definition: We study how RFQ design interacts with the time-cost trade-off in project execution and compare two common contract forms: time-incentive contracts (penalizing lateness) and cost-sharing contracts (reimbursing direct costs). Methodology/results: Using a game-theoretic model, we characterize optimal RFQ designs under incomplete information about contractors' direct cost efficiency. Time-incentive contracts weaken competition, whereas cost-sharing contracts strengthen it, increasing work rates and shortening completion times. Clients prefer cost-sharing contracts, with this preference being stronger when time urgency is high, contractor heterogeneity is substantial, or the contractor pool is small; time-incentive contracts yield higher overall system efficiency in low-urgency settings, consistent with their prevalence in public projects. We derive a closed-form bound on the relative decrease in the client's payoff when using a fixed-term RFQ instead of a more complex upfront-fee RFQ and numerically show that fixed-term RFQs lose less than 0.28% of client payoff. Audit noise has no effect when correction costs are negligible or very high, but it introduces bidding frictions and lowers the optimal cost-sharing ratio when correction costs are moderate. Managerial implications: These insights guide contract selection by time urgency and market conditions and support the use of simple fixed-term RFQs with minimal profit loss.

With the rapid growth of omnichannel retailing and the takeaway delivery economy, the classic point-to-point mode for on-demand delivery is deficient in delivery capacity, coverage area, dispatching efficiency, and courier safety assurance. Inspired by the success of Dabbawala, a historical Indian company for lunch delivery, we propose a novel public on-demand delivery service system that uses the public transit network to satisfy stochastic delivery demands. In particular, the proposed system includes a radial public transit network for intermediate transshipment, as well as couriers with e-bikes for terminal pick-up and drop-off. Our research aims to generate system design that minimizes the sum of penalty costs from lost sales and operational costs associated with courier terminal delivery distance. Solving the integrated system optimization problem relies on incorporating operational details, especially the allocation strategies of the lines’ capacity and the couriers’ terminal traveling modes. For the former, we propose a novel flexible design, called dual long-chain design, to improve flexibility. For the latter, we propose an elegant approximation of optimal service region partitioning that minimizes the expected terminal delivery distance and the resulting costs, without compromising delivery timeliness. Leveraging the theoretical results of the operational strategies, we simplify the integrated optimization problem and propose an efficient approximation algorithm. Finally, we validate the advantage of the proposed system over classic point-to-point delivery in satisfying demands and reducing costs through extensive numerical experiments, providing managerial insights in handling massive on-demand delivery demands and utilizing the idle capacity of the public transit system.

We study how households understand and respond to monetary policy by exploiting the open auctions of early-stage crowdfunding and inferring individuals’ expectations based on their maximum requested interest rates. Using loan listings from Prosper, we find that borrowers adjust their willingness-to-pay interest rates in response to unexpected Federal funds rate changes, whereas anticipated shifts have negligible effects. These responses are more pronounced among high-income, high-credit-score borrowers; large loan applicants; and when Federal Reserve communication is transparent. The responses are highly asymmetric—Borrowers sharply lower rates during unexpected easing but resist increasing them during unexpected tightening. The results are robust to alternative specifications, including regression-discontinuity-in-time designs and alternative measures of monetary policy shock. Lenders also respond to policy shocks and counteract borrowers’ adjustments. Analysis of Robinhood data shows that retail investors mirror this behavior by reducing equity holdings after surprise rate hikes.

Our study analyses 6 million civil judgments in China from 2014 to 2018, documenting gender disparities that disfavour female litigants. We investigate the impact of an open justice reform that mandated courts to broadcast legal proceedings live on a centralized online platform. By exploiting variations in its implementation across courts and over time and employing both difference-in-differences and Bartik IV approaches, we find that gender disparities in chances of winning decrease as broadcast intensity increases. Analysis of the textual content of judicial decisions provides further evidence that these changes in judicial outcomes stem from altered judge behaviours (i.e. attention and effort) under enhanced judicial transparency. Our results demonstrate how information technology shapes judges’ conduct, underscoring its broader potential to improve accountability in public institutions.

Utilizing 1.54 million judicial judgments from enterprise-to-enterprise litigation between 2014 and 2019 in China, we provide evidence of municipal leaders exerting influence over the courts to favor enterprises connected to them. By leveraging variations in enterprise connections resulting from official turnover, we show that enterprises with connections to party leaders have higher chances of winning in business litigation than unconnected enterprises. We also examine the impact of the staggered roll-out of circuit courts, a top-down institutional reform, on cronyism in the courtroom. Our findings show that this reform has effectively reduced the judicial advantage enjoyed by connected enterprises by two-thirds. By contrast, the trial live-broadcasting reform increases visibility but is not associated with a reduction in the effect of political connections, suggesting that different forms of judicial bias require different monitoring approaches.

Many emerging economies employ preferential credit policies that target selected sectors. This paper quantifies the implications of such policies for aggregate productivity and welfare. Using Chinese firm-level data from 2009–2020, we first document that sectors with higher markups receive larger credit subsidies and exhibit higher revenue-based productivity. Motivated by these facts, we develop a multi-sector quantitative model with endogenously determined markups and calibrate it to match the distribution of sales both within and across sectors. We find that preferential credit subsidies raise aggregate productivity and welfare by reallocating market shares toward high-markup sectors. These gains persist in an extended framework with endogenous firm entry.

We examine whether and how common ownership affects Environmental, Social, and Governance (ESG) ratings—an important research question given the increasing use of these ratings in investment decisions and corporate evaluations. We find that companies with major shareholders in common with the rating agency (“sister firms”) tend to receive higher ESG ratings. When a company becomes a sister firm through a change in the rating agency's ownership structure, its rating from that agency is subsequently upgraded, whereas its ESG ratings from other agencies remain unchanged. Sister firms exhibit greater rating disagreements across agencies than other firms. The higher ESG ratings for sister firms are partly attributable to the transfer of immaterial positive ESG information through common owners. The common ownership effect is more pronounced when the owner can exert a greater influence on the rating agency. Moreover, sister firms with initially elevated ratings demonstrate poorer future ESG performance. Overall, our findings suggest that owners can affect ESG ratings of their portfolio companies in a way consistent with their influence and interest.

Many omnichannel firms are switching from channel-specific pricing to uniform pricing, but little is known about the immediate and long-term effects of this change. Relying on a quasi-experiment setting featuring a pricing policy change varying across 4,150 products over 18 months, we empirically investigate the effects of uniform pricing on online and offline channel sales. We show that this transition is beneficial for both online and offline sales, with effects becoming increasingly positive over time. Furthermore, we show that the treatment effects can be decomposed into two contrasting effects: a delayed positive effect and an immediate negative effect on product sales. Last, in consumer-level analysis, we show that the treatment effects are due to two distinct behavioral patterns: a decrease in the shopping frequency of 77.4% of the consumers who can no longer leverage price differences between channels and an increase in the activity of 22.6% of consumers who appreciate the ease of search and the comprehensiveness of the omnichannel experience. This study sheds light on an important pricing decision in omnichannel retail—whether to switch from channel-specific to uniform pricing.