Allocating Stimulus Checks in Times of Crisis

We study the problem of allocating bailouts (stimulus, subsidy allocations) to people participating in a financial network subject to income shocks. We build on the financial clearing framework of Eisenberg and Noe that allows the incorporation of a bailout policy that is based on discrete bailouts...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:arXiv.org
Hlavní autori: Papachristou, Marios, Kleinberg, Jon
Médium: Paper
Jazyk:English
Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 29.04.2022
Predmet:
ISSN:2331-8422
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract We study the problem of allocating bailouts (stimulus, subsidy allocations) to people participating in a financial network subject to income shocks. We build on the financial clearing framework of Eisenberg and Noe that allows the incorporation of a bailout policy that is based on discrete bailouts motivated by the types of stimulus checks people receive around the world as part of COVID-19 economical relief plans. We show that optimally allocating such bailouts on a financial network in order to maximize a variety of social welfare objectives of this form is a computationally intractable problem. We develop approximation algorithms to optimize these objectives and establish guarantees for their approximation rations. Then, we incorporate multiple fairness constraints in the optimization problems and establish relative bounds on the solutions with versus without these constraints. Finally, we apply our methodology to a variety of data, both in the context of a system of large financial institutions with real-world data, as well as in a realistic societal context with financial interactions between people and businesses for which we use semi-artificial data derived from mobility patterns. Our results suggest that the algorithms we develop and study have reasonable results in practice and outperform other network-based heuristics. We argue that the presented problem through the societal-level lens could assist policymakers in making informed decisions on issuing subsidies.
AbstractList We study the problem of allocating bailouts (stimulus, subsidy allocations) to people participating in a financial network subject to income shocks. We build on the financial clearing framework of Eisenberg and Noe that allows the incorporation of a bailout policy that is based on discrete bailouts motivated by the types of stimulus checks people receive around the world as part of COVID-19 economical relief plans. We show that optimally allocating such bailouts on a financial network in order to maximize a variety of social welfare objectives of this form is a computationally intractable problem. We develop approximation algorithms to optimize these objectives and establish guarantees for their approximation rations. Then, we incorporate multiple fairness constraints in the optimization problems and establish relative bounds on the solutions with versus without these constraints. Finally, we apply our methodology to a variety of data, both in the context of a system of large financial institutions with real-world data, as well as in a realistic societal context with financial interactions between people and businesses for which we use semi-artificial data derived from mobility patterns. Our results suggest that the algorithms we develop and study have reasonable results in practice and outperform other network-based heuristics. We argue that the presented problem through the societal-level lens could assist policymakers in making informed decisions on issuing subsidies.
Author Papachristou, Marios
Kleinberg, Jon
Author_xml – sequence: 1
  givenname: Marios
  surname: Papachristou
  fullname: Papachristou, Marios
– sequence: 2
  givenname: Jon
  surname: Kleinberg
  fullname: Kleinberg, Jon
BookMark eNotjctKxDAUQIMoOI7zAe4CrlNvbh5NlkPxBQMu7H5I00QzdlJtWvHzHdDV4WzOuSLnecyBkBsOlTRKwZ2bftJ3hRx0BbXScEZWKARnRiJekk0pBwBAXaNSYkXYdhhG7-aU3-jrnI7LsBTavAf_UWjKtE3HUOgYaTOlkso1uYhuKGHzzzVpH-7b5ontXh6fm-2OOYWGWVsb7OvIO80xxF71ELQVOqLpIHoDTlnrvPUy6JpLffLQgXQInY7GSbEmt3_Zz2n8WkKZ94dxmfLpuEclOUcw1ohfgK5E3Q
ContentType Paper
Copyright 2022. This work is published under http://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2022. This work is published under http://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
COVID
DWQXO
HCIFZ
L6V
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
DOI 10.48550/arxiv.2106.07560
DatabaseName ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials - QC
ProQuest Central (New)
ProQuest Technology Collection
ProQuest One
Coronavirus Research Database
ProQuest Central Korea
SciTech Premium Collection
ProQuest Engineering Collection
Engineering Database
Proquest Central Premium
ProQuest One Academic (New)
ProQuest Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
Engineering Collection
DatabaseTitle Publicly Available Content Database
Engineering Database
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
Coronavirus Research Database
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
ProQuest One Academic UKI Edition
ProQuest Central Korea
Materials Science & Engineering Collection
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
Engineering Collection
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Physics
EISSN 2331-8422
Genre Working Paper/Pre-Print
GroupedDBID 8FE
8FG
ABJCF
ABUWG
AFKRA
ALMA_UNASSIGNED_HOLDINGS
AZQEC
BENPR
BGLVJ
CCPQU
COVID
DWQXO
FRJ
HCIFZ
L6V
M7S
M~E
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
ID FETCH-LOGICAL-a528-99782d7f1b612efd5d0e6936f28b0fc80a599ac9c4e671460a5eb04a20b6f8a43
IEDL.DBID M7S
IngestDate Mon Jun 30 09:24:48 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a528-99782d7f1b612efd5d0e6936f28b0fc80a599ac9c4e671460a5eb04a20b6f8a43
Notes SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
OpenAccessLink https://www.proquest.com/docview/2541120898?pq-origsite=%requestingapplication%
PQID 2541120898
PQPubID 2050157
ParticipantIDs proquest_journals_2541120898
PublicationCentury 2000
PublicationDate 20220429
PublicationDateYYYYMMDD 2022-04-29
PublicationDate_xml – month: 04
  year: 2022
  text: 20220429
  day: 29
PublicationDecade 2020
PublicationPlace Ithaca
PublicationPlace_xml – name: Ithaca
PublicationTitle arXiv.org
PublicationYear 2022
Publisher Cornell University Library, arXiv.org
Publisher_xml – name: Cornell University Library, arXiv.org
SSID ssj0002672553
Score 1.7931678
SecondaryResourceType preprint
Snippet We study the problem of allocating bailouts (stimulus, subsidy allocations) to people participating in a financial network subject to income shocks. We build...
SourceID proquest
SourceType Aggregation Database
SubjectTerms Algorithms
Allocations
Approximation
Bailouts
Context
Optimization
Subsidies
Title Allocating Stimulus Checks in Times of Crisis
URI https://www.proquest.com/docview/2541120898
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05T8MwFLagBYmJWxylysBq6jhOYk8IqlYwUEW0Q5kqnyKiTUvSVvx8bJMCEwuj7cXPx_cOP38PgGuVGh1RjqEQmEDCVAIZkgZyEVLJ7CFRSvliE-lgQMdjltUBt6pOq9xgogdqNZcuRt6xjow1DRBl9HbxDl3VKPe6WpfQ2AZNx5KAfere8DvGgpPUWszR12Omp-7q8PIjX99YP8dxdsY1MeVvCPZ6pb__3xkdgGbGF7o8BFu6OAK7Pp9TVscA3k2dlnI5zcFwmc9W01UVdF-1fKuCvAj8x49gboKuveN5dQJG_d6o-wDrygiQx9gClHX9sF3lUFj7RBsVK6QTFiUGU4GMpIjHjHHJJNFJaqHQtrVAhGMkEkM5iU5Bo5gX-gwERNJY2-6QGEK0su6LwKkOuWOFSbTg56C1EX5Sn-5q8iP5xd_Dl2APu-8CiEDMWqCxLFf6CuzI9TKvyjZo3vcG2XPbb5ptZY9P2csn7aehZg
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V07T8MwED6VAoKJt3iTAUZD6jiJPSCEChVVS1WpHbpVfkVEQAtNW-BH8R85hwaY2BgYbUuWdT5_58--B8CxiRMbcEmJUpQRJkxEhK8TIlWFa4FKYozJi03ErRbv9US7BO9FLIxzqywwMQdqM9TujfwMiQxeDXwu-MXTM3FVo9zvalFC41MtGvbtBSlbdl6_wv09obR23a3ekFlVASJDiocbaRPFFVYU2nabmND4NhJBlFCu_ERzX4ZCSC00s1GMMIJtq3wmqa-ihEsW4LRzMM8c-Oeegp2vJx0axXhBDz7_TvNMYWdy9JpOT5FWuRSh4SwP5k_Ez81YbeWfCWAV5tvyyY7WoGQH67CYe6vqbAPI5YOzwc5j2-uM08fJwyTzqndW32deOvDysBZvmHhVRLA024TuXyxwC8qD4cBug8c0Dy12V1jCmDVIzhSNbUW6nDeRVXIH9gtZ92dnN-t_C3r39-EjWLrp3jb7zXqrsQfL1AVG-IxQsQ_l8WhiD2BBT8dpNjrM9cSD_h9vywcrnfl5
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Allocating+Stimulus+Checks+in+Times+of+Crisis&rft.jtitle=arXiv.org&rft.au=Papachristou%2C+Marios&rft.au=Kleinberg%2C+Jon&rft.date=2022-04-29&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.2106.07560