Endogenous Production Networks under Uncertainty

Kopytov, Alexandr; Mishra, Bineet; Nimark, Kristoffer; Taschereau-Dumouchel, Mathieu

doi:10.2139/ssrn.3936969

Cited by 6 publications

(14 citation statements)

References 33 publications

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“…Typically, these models consider upstream and downstream firm decisions in risky multi-tier production networks. 2022) simulate an optimal strategy to aggregate shocks that perish all suppliers in a particular region, Kopytov et al (2023) simulate an optimal strategy to aggregate productivity shocks that hit all suppliers in a particular sector. Our framework is closer to Jiang et al (2022) in terms of modelling shocks (reduced-form aggregate destructive events hitting one location), but closer to Kopytov et al (2023) in terms of upstream and downstream interactions (firms can adjust their input sourcing decisions through both intensive and extensive margins at the beginning of the period, while once the production process has started only the intensive margin is active until the end of the period).…”

Section: Previous Literaturementioning

confidence: 99%

“…In order to investigate the relationship between the upstream supplier diversification and the chain-level robustness (survival probability), we augment a parsimonious supply chain model of Antras and de Gortari (2020) with uncertainty as in Jiang et al (2022) and Kopytov et al (2023) and simulate alternative upstream sourcing strategies in presence of information incompleteness and uncertainty of aggregate shocks. The key characteristics and channels of adjustment relevant to the analysis along with the main intuition of the modelling framework are explained in this section.…”

Section: Simulationsmentioning

confidence: 99%

“…12 Uncertainty in the baseline model is introduced following Jiang et al ( 2022) and Kopytov et al (2023).…”

Section: Partial Information and Uncertainty 12mentioning

confidence: 99%

“…Kopytov et al. (2023) investigate how a supply chain's uncertainty affects firms' sourcing decisions and how individual firm decisions affect the aggregate macro economy. Whereas Jiang et al.…”

Section: Previous Literaturementioning

confidence: 99%

“…Final goods can be produced using different sets of intermediate inputs that can be sourced from any location. Kopytov et al (2023) label these firm decisions as techniques. Slightly differently in our model, a technique is a production function that specifies from which locations and suppliers intermediate inputs to source.…”

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Uncertainty of supply chains: Risk and ambiguity

Kancs

2023

World Economy

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Motivated by the recently experienced systemic shocks (the COVID‐19 pandemic and the full‐fledged Russia's war of aggression against Ukraine)—that have created new forms of uncertainties to our supplies—this paper explores the supply chain robustness under risk aversion and ambiguity aversion. We aim to understand the potential consequences of deeply uncertain systemic events on the supply chain resilience and how does the information precision affect individual agents' choices and the chain‐level preparedness to aggregate shocks. Augmenting a parsimonious supply chain model with uncertainty, we analyse the relationship between the upstream sourcing decisions and the supply chain survival probability. Both risk‐averse and ambiguity‐averse individually‐optimising agents' upstream sourcing paths are efficient but can become vulnerable to aggregate shocks. In contrast, a chain‐level coordination of downstream firm sourcing decisions can qualitatively improve the robustness of the entire supply chain compared to the individual decision‐making baseline. Such a robust decision making ensures that in the presence of an aggregate shock—independently of its realisation—part of upstream suppliers will survive and the final goods' supply will be ensured even under the most demanding circumstances. Our results also indicate that an input source diversification extracts a cost in foregone efficiency.

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Section: Previous Literaturementioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

“…12 Uncertainty in the baseline model is introduced following Jiang et al ( 2022) and Kopytov et al (2023).…”

Section: Partial Information and Uncertainty 12mentioning

confidence: 99%

Section: Previous Literaturementioning

confidence: 99%

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Uncertainty of supply chains: Risk and ambiguity

Kancs

2023

World Economy

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Rigid production networks

Pellet

Tahbaz-Salehi

2023

Journal of Monetary Economics

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Micro Propagation and Macro Aggregation

Baqaee,

Rubbo

2023

Annual Review of Economics

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This article reviews a framework for studying the aggregation and propagation of microeconomic shocks in general equilibrium. We discuss the determinants of aggregate measures of real economic activity, like real GDP, real domestic absorption, and aggregate productivity in both efficient and inefficient environments. We also discuss how shocks from one set of producers are transmitted to other producers through prices and quantities. The framework we provide is amenable to generalization and can be used to study any collection of producers ranging from one isolated producer to an industry consisting of heterogeneous producers to an entire economy. We conclude with a brief survey of some of the applied questions that can be addressed using the analytical tools presented in this review and avenues for future work.

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Endogenous Production Networks under Uncertainty

Cited by 6 publications

References 33 publications

Uncertainty of supply chains: Risk and ambiguity

Uncertainty of supply chains: Risk and ambiguity

Rigid production networks

Micro Propagation and Macro Aggregation

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