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We study systemic risk in a supply chain network where firms are connected through purchase orders. Firms can be hit by cost or demand shocks, which can cause defaults. These shocks propagate through the supply chain network via input-output linkages between buyers and suppliers. Firms endogenously take contingency plans to mitigate the impact generated from disruptions. We show that, as long as firms have large initial equity buffers, network fragility is low if both buyer diversification and supplier diversification are low. We find that a single-sourcing strategy is beneficial for a firm only if the default probability of the firm’s supplier is low. Otherwise, a multiple-sourcing strategy is ex post more cost effective for a firm. Funding: J.R. Birge acknowledges financial support from the University of Chicago Booth School of Business. The research of A. Capponi has been supported by the NSF/CMMI CAREER-1752326 award. P.-C. Chen acknowledges financial support from the Research Grant Council of Hong Kong [Early Career Scheme Grant 27210118]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2409 . 

Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions, we study a spatial epidemic model that explicitly accounts for population movements and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline nonteleworkable employment level. By contrast, uniform restriction policies that achieve the same policy goal permit 3.92–6.25 times less nonteleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic.

This paper was accepted by Carri Chan, healthcare management.

 

The rapid growth of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, small natural gas-fueled generators, combined heat and power plants, etc. Due to the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper an efficient aggregation model where a profit-maximizing aggregator procures electricity from DERs, and sells them in the wholesale market. The interaction between the aggregator and the DER owners is modeled as a Stackelberg game: the aggregator adopts two-part pricing by announcing a participation fee and a per-unit price of procurement for each DER owner, and the DER owner responds by choosing her payoff-maximizing energy supplies. We show that our proposed model preserves full market efficiency, i.e., the social welfare achieved by the aggregation model is the same as that when DERs participate directly in the wholesale market. 

Connections across commodity markets create the potential for risk to propagate and for failures to cascade as successive market agents fail. The structure of these networks is, however, often hidden and not directly observable. This article describes methods to uncover this hidden structure and the implications that these hidden connections may have for predicting risk propagation and cascading failures. The results are described in the context of electricity, gasoline, and financial markets. They illustrate the potential of this methodology to help address energy and commodity policy issues and their environmental implications.