Shock Next Door: Geographic Spillovers in FinTech Lending After Natural Disasters

We examine geographic spillovers in digital credit markets by studying how natural disasters affect borrowing behavior in adjacent, physically undamaged regions. Using granular loan-level data from Indonesia’s largest FinTech lender (2021–2023) and leveraging quasi-random variation in disaster timing and location, we estimate fixed-effects specifications that incorporate spatially lagged disaster exposure (an SLX-type spatial approach) to quantify spillovers. Disasters generate economically significant spillovers in neighboring provinces: a 1% increase in disaster frequency raises local borrowing by 0.036%, approximately 20% of the direct effect. Spillovers vary sharply with geographic connectivity—land-connected provinces experience effects about 6.6 times larger than sea-connected provinces. These results highlight that digital lending platforms can transmit geographically proximate risks beyond directly affected areas through channels that differ from traditional banking networks. The systematic nature of these spillovers suggests that disaster-response strategies may be more effective when they consider adjacent regions. That platform risk management can be strengthened by integrating spatial disaster exposure and connectivity into credit monitoring and decision rules.

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Publication Details

Journal
Econometrics
Published
2026-01-15
DOI
https://doi.org/10.3390/econometrics14010005
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00
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article

Shock Next Door: Geographic Spillovers in FinTech Lending After Natural Disasters

Weibiao Xu, Ding Ding, Yue Wang, Jianzheng Shi
Econometrics
FinTech, Crowdfunding, Digital Finance
article

Shock Next Door: Geographic Spillovers in FinTech Lending After Natural Disasters

Weibiao Xu, Ding Ding, Yue Wang, Jianzheng Shi
article en

Abstract

We examine geographic spillovers in digital credit markets by studying how natural disasters affect borrowing behavior in adjacent, physically undamaged regions. Using granular loan-level data from Indonesia’s largest FinTech lender (2021–2023) and leveraging quasi-random variation in disaster timing and location, we estimate fixed-effects specifications that incorporate spatially lagged disaster exposure (an SLX-type spatial approach) to quantify spillovers. Disasters generate economically significant spillovers in neighboring provinces: a 1% increase in disaster frequency raises local borrowing by 0.036%, approximately 20% of the direct effect. Spillovers vary sharply with geographic connectivity—land-connected provinces experience effects about 6.6 times larger than sea-connected provinces. These results highlight that digital lending platforms can transmit geographically proximate risks beyond directly affected areas through channels that differ from traditional banking networks. The systematic nature of these spillovers suggests that disaster-response strategies may be more effective when they consider adjacent regions. That platform risk management can be strengthened by integrating spatial disaster exposure and connectivity into credit monitoring and decision rules.

EconometricsVol. 14(1)
Singapore University of Social Sciences (SG)
Climate action
Openalex Percentile: Top 90%
FinTech, Crowdfunding, Digital Finance
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Shock Next Door: Geographic Spillovers in FinTech Lending After Natural Disasters — Weibiao Xu, Ding Ding, et al. · Econometrics (2026) | TGRS Research Map | TGRS