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  Repayment under flexible loan contracts: evidence based on high-frequency data

Grohmann, A., Lenel, F., Herbold, S. (2025 online): Repayment under flexible loan contracts: evidence based on high-frequency data. - Economica, 93, 369, 160-187.
https://doi.org/10.1111/ecca.70013

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Datensatz-Permalink: https://publications.pik-potsdam.de/pubman/item/item_32554 Versions-Permalink: https://publications.pik-potsdam.de/pubman/item/item_32554_2
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 Urheber:
Grohmann, Antonia1, Autor
Lenel, Friederike2, Autor           
Herbold, Steffen1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 Zusammenfassung: We study repayment in the context of an alternative financial product that enables the purchase of a large asset—a solar panel home system—while offering complete repayment flexibility. Using a large administrative data set on daily repayment of 38,400 borrowers in Tanzania over 5.5 years, we perform unsupervised pattern analysis to classify repayment behavior. We show that borrowers with fluctuating incomes make more use of the loan’s flexibility and adjust their repayment to cash flow. In further analysis, we show that in particular farmers use the flexibility provided by the loan contract to adjust to seasonal fluctuations and to deal with unexpected shocks. Our results indicate that low-income households can finance large assets through innovative financial approaches that allow aligning payments to financial circumstances.

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Sprache(n): eng - English
 Datum: 2021-05-252025-07-082025-10-22
 Publikationsstatus: Online veröffentlicht
 Seiten: 28
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: MDB-ID: No data to archive
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Adaptation in Agricultural Systems
Research topic keyword: Economics
Research topic keyword: Sustainable Development
Model / method: Econometrics
Model / method: Machine Learning
Regional keyword: Africa
OATYPE: Hybrid Open Access
DOI: 10.1111/ecca.70013
 Art des Abschluß: -

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Titel: Economica
Genre der Quelle: Zeitschrift, SSCI, Scopus
 Urheber:
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 93 (369) Artikelnummer: - Start- / Endseite: 160 - 187 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1468-0335
Publisher: Wiley