1,720,978 research outputs found
Disparità razziali in Usa nella concessione dei prestiti: la riforma del Paycheck Protection Program
No full textIntroduced under the Trump-Pence Administration, small American businesses relied on the Paycheck Protection Program (Ppp) as a source of short-term relief loans during the heights of the Coronavirus pandemic. The initial design of the Ppp was heavily criticised by researchers as racial disparities, amongst others, existed within its lending process. Minority groups received less Ppp loan amounts during the original two tranches released in 2020. Aiming to increase equitable access to all, in February 2021 the Biden-Harris Administration enforced swift changes to the initial Ppp aimed at favouring access to Ppp loans for minority-owned small businesses that were disadvantaged by the original version of the program design. By exploiting a granular dataset of 2.1 million Ppp loans granted between Q2 2020 - Q2 2021 and by implementing a difference-in differences approach (Did), this paper provides novel evidence on the effectiveness of the Biden reforms in reducing racial disparities within the Paycheck Protection Program
Did Biden-Harris’s Reforms on the Paycheck Protection Program Reduce Racial Disparities in Lending?
Full text linkIntroduced under the Trump-Pence Administration, the Paycheck Protection Program (PPP) provided short-term relief loans to small American businesses during the peak of the Coronavirus pandemic. The initial design of the PPP faced significant criticism from researchers due to racial disparities, among other issues, in its lending process. Minority groups received smaller PPP loan amounts during the original two tranches released in 2020. To increase equitable access for all, in February 2021 the Biden-Harris Administration enforced swift changes to the initial PPP aimed at favouring access to PPP loans for minority-owned small businesses that had been disadvantaged by the program’s original design under the Trump-Pence Administration. By exploiting a granular dataset of 1,759,270 PPP loans granted between Q2 2020 and Q2 2021 and by implementing a difference-in-differences approach (DiD), this paper provides novel evidence on the effectiveness of the Biden reforms in reducing racial bias within the Paycheck Protection Program. Indeed, we observe a significant increase in the volume of PPP loans granted to minority-owned businesses in the period following the Biden-Harris Administration’s reforms. Furthermore, among different minority groups, the reforms appear most effective for Native American minority groups (including American Indians, Alaska Natives, Native Hawaiians and/or Other Pacific Islanders), followed by Black Americans and Asian business owners. Our findings offer novel contributions to the existing literature on institutional discrimination, particularly regarding the initial PPP design. Our findings are especially valuable for policy makers as they underscore the importance of radical changes in addressing racial disparities. Our paper also offers evidence of how a public credit guarantee program should be designed to empower and promote economic inclusion for all, regardless of ethnicity, aligning with the UN's Sustainable Development Goals
Minguzzi A., Modina M., Filomeni S., Bredice M. Un esperienza di crowdfunding sociale come come strumento di sussidiarietà verticale. Finanziare o gestire una piattaforma modifica l'effetto di impatto sociale di una Fondazione di origine bancaria?, in A.A.V.V. (2023), XVII Colloquio Scientifico sull’impresa sociale. XVII edizione del Colloquio Scientifico sull’impresa sociale, Perugia, 9-10 giugno 2023, Iris Network.
No full textLo studio delle dinamiche dell’imprenditorialità sociale è stato fortemente influenzato dai crescenti processi di digitalizzazione che hanno modificato le modalità di funzionamento sia delle imprese sociali che degli operatori che le sostengono. Negli ultimi decenni questi fenomeni hanno creato nuovi modelli – come il crowdfunding - attraverso cui, prima le piccole imprese for profit, e poi le imprese sociali, hanno cercato di soddisfare la loro richiesta di beneficenza e di equity. Nello stesso tempo le grant making foundation hanno a loro volta valutato l’utilità dello strumento dal punto di vista dell’ottimizzazione dell’impatto sociale della loro azione filantropica. In questo lavoro è stata osservata l’attività di una piattaforma di crowdfunding sociale (Meridonale srl) creata da una grant making foundation di origine bancaria (Fondazione Banco di Napoli) analizzando tutte le operazioni compiute in oltre due anni di attività per comprendere le determinanti del
successo dell’iniziativa e le sue modalità di misurazione. Il database che ha raccolto e misurato i fenomeni relazionali e finanziari sviluppati è stato elaborato evidenziando dei risultati significativi in merito ai fattori critici di successo. Nonostante la piattaforma permetta di raggiungere una maggiore platea di raccolta che in passato emerge una significativa immaturità digitale della beneficenza che è ancora significativamente influenzata da fattori umani (human touch) oltre che da fattori di scala (strutturali e dimensionali). Infine lo studio mette in luce come i risultati della piattaforma possano essere misurati in termini di “leva sociale”
generata dai multipli finanziari raccolti dalla Fondazione rispetto all’investimento diretto operato. Tali evidenze rappresentano un risultato significativo sia per il dibattito scientifico in cui il crowdfunding è ancora poco analizzato per gli effetti di sussidiarietà sistemica che genera tra le imprese finanziatrici e le imprese
beneficiate sia per le implicazioni operative che arricchiscono le riflessioni utili per la gestione delle piattaforme di crowdfunding sociale
Can soft information survive organizational distance? Evidence from SMB lending
No full textThis paper examines whether and how soft information in corporate loan applications influences the lending decision process, and whether organizational frictions moderate this influence. In this context, soft information refers to the text data collected, analyzed, and transmitted by loan officers to the bank’s headquarters. Using a proprietary dataset from a large European bank, the study finds that soft information impacts both the outcomes and the speed of lending decisions. However, this effect is moderated by the organizational distance between the loan officer and the bank’s headquarters. Additionally, we provide evidence that soft information embedded in comments serves as a strong predictor of company defaults, supporting its use in lending to small and medium-sized businesses (SMBs), particularly when branches are distant from the bank’s headquarters. Our findings remain robust across additional analyses and tests, consistent with the presence of communication frictions within banking organizations that hinder the effective "hardening" of soft information as organizational distance increases
Can unlisted firms benefit from market information? A data-driven approach
Full text link[EN] We employ a sample of 10,136 Italian micro-, small-, and mid-sized enterprises (MSMEs) that borrow from 113 cooperative banks to examine whether market pricing of public firms adds additional information to accounting measures in predicting default of private firms. Specifically, we first match the asset prices of listed firms following a data-driven clustering by means of Neural Networks Autoencoder so to evaluate the firm-wise probability of default (PD) of MSMEs. Then, we adopt three statistical techniques, namely linear models, multivariate adaptive regression spline, and random forest to assess the performance of the models and to explain the relevance of each predictor. Our results provide novel evidence that market information represents a crucial indicator in predicting corporate default of unlisted firms. Indeed, we show a significant improvement of the model performance, both on class-specific (F1-score for defaulted class) and overall metrics (AUC) when using market information in credit risk assessment, in addition to accounting information. Moreover, by taking advantage of global and local variable importance technique we prove that the increase in performance is effectively attributable to market information, highlighting its relevant effect in predicting corporate default.Bitetto, A.; Filomeni, S.; Modina, M. (2022). Can unlisted firms benefit from market information? A data-driven approach. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 65-72. https://doi.org/10.4995/CARMA2022.2022.15045OCS657
Communication frictions in banking organizations: Evidence from credit score lending
Full text linkBy using mid-corporate loan-level data on loan approval decisions collected from a large European bank, we investigate whether spatially-based bank organizational frictions affect borrowers’ credit availability
Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses
Full text linkIn this paper, we compare two different approaches to estimate the credit risk for small- and mid-sized businesses (SMBs), namely a classic parametric approach, by fitting an ordered probit model, and a non-parametric approach, calibrating a machine learning historical random forest (HRF) model. The models are applied to a unique and proprietary dataset comprising granular firm-level quarterly data collected from a European investment bank and an international insurance company on a sample of 464 Italian SMBs over the period 2015–2017. Results show that the HRF approach outperforms the traditional ordered probit model, highlighting how advanced estimation methodologies that use machine learning techniques can be successfully implemented to predict SMB credit risk, i.e. when facing high asymmetries of information. Moreover, by using Shapley values, we are able to assess the relevance of each variable in predicting SMB credit risk
Can we trust machine learning to predict the credit risk of small businesses?
Full text linkWith the emergence of Fintech lending, small firms can benefit from new channels of financing. In this setting, the creditworthiness and the decision to extend credit are often based on standardized and advanced machine-learning techniques that employ limited information. This paper investigates the ability of machine learning to correctly predict credit risk ratings for small firms. By employing a unique proprietary dataset on invoice lending activities, this paper shows that machine learning techniques overperform traditional techniques, such as probit, when the set of information available to lenders is limited. This paper contributes to the understanding of the reliability of advanced credit scoring techniques in the lending process to small businesses, making it a special interesting case for the Fintech environment
Access to Credit in a Market Downturn
Full text linkUsing a unique proprietary dataset from a large European commercial bank containing granular loan-level information on credit lines to mid-corporate firms, we investigate the bank’s decisions to allow firms to retain existing credit at a time of acute financial instability. Our results highlight the importance of bank-firm relationships during crisis times. Existing borrowers who actively used their credit lines were not rationed, unless they posed an increased credit risk. We do not find evidence of evergreening practices
Collaboration or community? The impact of the institutional forces in promoting social crowdfunding
No full textThe paper explores whether social crowdfunding triggers the success of social pro-jects by focusing on the role of an Italian platform. By examining 140 projects be-tween 2016 and 2018, our study analyzes how the platform acts in facilitating the in-teraction between non-profit organizations and private investors willing to participate in the financing of social projects. Our results support the relevant role a social crowdfunding platform has on the success of a campaign. The involvement of the population through actions that leverage the human-touch relationship, and the social nature of the project increases the propensity to achieve the funding objectives
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