Shocked by Bank Funding Shocks: Evidence from 500 Million Consumer Credit Cards

Chava, Sudheer; Ganduri, Rohan; Paradkar, Nikhil; Zeng, Linghang

doi:10.2139/ssrn.3509356

Cited by 6 publications

(5 citation statements)

References 47 publications

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“…Our paper relates primarily to literature on fintech adoption and the role of shocks in driving acceleration. Existing studies have highlighted that idiosyncratic shocks within countriesaffecting either the demand or supply-side-can have immediate and longer-term effects on local rates of fintech adoption (Higgins (2019), Crouzet et al (2019), Chava et al (2018)). 5 These studies tend to emphasize how network effects may either amplify or lessen rates of adoption during a shock.…”

Section: Introductionmentioning

confidence: 99%

The Global Impact of COVID-19 on Fintech Adoption

Mishra

2020

SSRN Journal

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Section: Introductionmentioning

confidence: 99%

The Global Impact of COVID-19 on Fintech Adoption

Mishra

2020

SSRN Journal

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“…Figure 1 shows that debt on revolving accounts rose from 0% to 9% of disposable personal income over three decades, then dropped by almost one‐third after the Financial Crisis. What this accumulation of debt and the accompanying changes in credit tell us have been central to the literature (Agarwal et al., 2017, Aydin, 2015, Chava et al., 2019, Fulford and Schuh, 2019, Gross and Souleles, 2002, Gross, Notowidigdo, and Wang, 2020). Understanding why and when consumers revolve credit card debt from month‐to‐month is a central focus of several puzzles in consumer finance (Agarwal, Skiba, and Tobacman, 2009, Bertaut, Haliassos, and Reiter, 2009, Laibson, Repetto, and Tobacman, 2003).…”

Section: Figurementioning

confidence: 99%

“…Convenience users are much less sensitive to changes in their credit limits. When we estimate the impact of a change in credit on all consumers, we closely match estimates of exogenous changes to credit that do not distinguish between revolvers and convenience users (Chava et al., 2019, Gross, Notowidigdo, and Wang, 2020).…”

Section: Figurementioning

confidence: 99%

“…As such, they confront many of the same empirical facts, 3. See, for example, Chava et al (2019) who used exogenous variation in credit limits to examine the pass-through from credit to debt and finds relatively large pass-through, but also significant heterogeneity. Gross, Notowidigdo, and Wang (2020) examine the increase in credit card limits and credit card debt following the aging off of a bankruptcy flag from credit reports to understand the marginal propensity to consume over time.…”

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confidence: 99%

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Revolving versus Convenience Use of Credit Cards: Evidence from U.S. Credit Bureau Data

Fulford¹,

Schuh²

2023

J of Money Credit Banking

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Credit card payments and revolving debt are important for consumer theory but a key data source—credit bureau records—does not distinguish between current charges and revolving debt. We develop a theory‐based econometric methodology using a hidden Markov model to estimate the likelihood a consumer is revolving debt each quarter. We validate our approach using a new survey linked to credit bureau data. We estimate that for likely revolvers: (i) 100% of an increase in credit becomes an increase in debt eventually; (ii) credit limit changes are half as salient as debt changes; and (iii) revolving status is persistent.

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“…Digital finance adoption has both long-term and short-term connotations (Fu and Mishra, 2020). The amplification or reduction in the adoption of FinTech is dependent on idiosyncratic shock within the country (Chava et al. , 2018; Crouzet et al.…”

Section: Introductionmentioning

confidence: 99%

The interplay of perceived risk, perceive benefit and generation cohort in digital finance adoption

Jain

Raman

2022

EMJB

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PurposeFinancial service providers are facing challenges in the acceptance of digital financial services. The study, therefore, intends to identify factors contributing towards the adoption of digital finance. It has worked on the influencers and demotivators of digital finance adoption by individuals. These influencers are labelled as perceived benefits and demotivators as perceived risks. In addition to perceived benefit and risk, the study has also included the difference in perception on the basis of generation cohort.Design/methodology/approachThe data have been collected through a structured questionnaire from 411 respondents. Partial least squares structured equation modelling (PLS-SEM) has been used to analyse the proposed model on SmartPLS.FindingsThe findings suggested that the benefits were more influential in adoption behaviour than perceived risk. In addition to perceived benefit and risk, the study has also included the difference in perception on the basis of generation cohort. The results summarised that benefits had a more significant impact in Generation Z (Gen Z) than in Millennials.Research limitations/implicationsThe evaluation and categorisation of perceived risk and benefits into meaningful dimensions generate value to the adoption behaviour of digital finance. Thus, the findings are useful for the policymakers and researchers to contemplate the perception of individuals in digital finance based on the generation cohort.Originality/valueThe empirical findings of the present research contribute to limited evidence of a relationship between perceived risk, perceived benefit and digital finance adoption on the basis of generation cohort.

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Shocked by Bank Funding Shocks: Evidence from 500 Million Consumer Credit Cards

Cited by 6 publications

References 47 publications

The Global Impact of COVID-19 on Fintech Adoption

The Global Impact of COVID-19 on Fintech Adoption

Revolving versus Convenience Use of Credit Cards: Evidence from U.S. Credit Bureau Data

The interplay of perceived risk, perceive benefit and generation cohort in digital finance adoption

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