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on Financial Literacy and Education |
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Issue of 2019‒12‒16
five papers chosen by |
| By: | The 11th International Workshop And Conference Of Asean Studies In Linguistics, Islamic And Arabic Education, Social Sciences And Educational Technology 2018; , murviana; , Marliyah; Ardiana, Asma |
| Abstract: | This paper has been presenting at The 11th International Workshop And Conference Of Asean Studies In Linguistics,Islamic And Arabic Education, Social Sciences And Educational Technology 2018 in Kisaran, North Sumatera, Indonesian on 7 May 2018 |
| Date: | 2018–06–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:inarxi:zgryx&r=all |
| By: | Valeria Venturelli; Giovanni Gallo; Alessia Pedrazzoli |
| Abstract: | In constructing online alternative finance instruments as a new form of financial democratization and financial inclusion, this article aims at verifying the presence of similarity effect in equity crowdfunding investments. Discussion focuses on ethnic and gender similarity between the seekers and investors that sustained the project. Our analysis is based on 5,996 personal investors that have participated in 81 equity crowdfunding campaigns, on Crowdcube, a British equity crowdfunding platform from 2011 and 2016. Results show that in equity crowdfunding gender and ethnic similarities play different role based on investors’ characteristics - gender, ethnicity and the combination of two. In particular, ethnic similarity positively influence the level of amount invested by both female and male investors belonging to an ethnic minority. Even if female investors tend to prefer male company, their preference changes if a female proponent belonging to an ethnic minority runs the company. From a practical perspective, our findings shed new light on how individual characteristics can be important factor in financing situations. Results allow entrepreneurs and equity crowdfunding platforms to understand better potential investor behaviour and highlights the role of equity crowdfunding as tool for minorities’ financial inclusion and women entrepreneur empowerment. |
| Keywords: | equity crowdfunding, entrepreneurial finance, ethnicity, gender, similarity effect |
| JEL: | G02 G11 M13 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:mod:wcefin:0080&r=all |
| By: | Antonio Lemus; Cristian Rojas |
| Abstract: | This paper questions the role that credit unions play in the Chilean financial system, particularly if they allow financial inclusion. For this purpose, using unique statistical information existing at the CMF, the credit and savings behavior of credit unions’ members is studied, with granularity at the individual level. The results indicate that credit unions effectively contribute to financial inclusion, providing financial services mostly to people with low incomes, elderly, women, and inhabitants of small communities, far from the large urban centers. |
| Keywords: | credit unions, financial inclusion, credit |
| JEL: | G21 G28 P13 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2019-27&r=all |
| By: | Christophe Hurlin (LEO - Laboratoire d'Économie d'Orleans - CNRS - Centre National de la Recherche Scientifique - Université de Tours - UO - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | In this article, we discuss the contribution of Machine Learning techniques and new data sources (New Data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of Machine Learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of Machine Learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through Machine Learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers' creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, Machine Learning applied to these data can also lead to severe biases and discrimination. |
| Abstract: | Dans cet article, nous proposons une réflexion sur l'apport des techniques d'apprentissage automatique (Machine Learning) et des nouvelles sources de données (New Data) pour la modélisation du risque de crédit. Le scoring de crédit fut historiquement l'un des premiers champs d'application des techniques de Machine Learning. Aujourd'hui, ces techniques permettent d'exploiter de « nouvelles » données rendues disponibles par la digitalisation de la relation clientèle et les réseaux sociaux. La conjonction de l'émergence de nouvelles méthodologies et de nouvelles données a ainsi modifié de façon structurelle l'industrie du crédit et favorisé l'émergence de nouveaux acteurs. Premièrement, nous analysons l'apport des algorithmes de Machine Learning à ensemble d'information constant. Nous montrons qu'il existe des gains de productivité liés à ces nouvelles approches mais que les gains de prévision du risque de crédit restent en revanche modestes. Deuxièmement, nous évaluons l'apport de cette « datadiversité », que ces nouvelles données soient exploitées ou non par des techniques de Machine Learning. Il s'avère que certaines de ces données permettent de révéler des signaux faibles qui améliorent sensiblement la qualité de l'évaluation de la solvabilité des emprunteurs. Au niveau microéconomique, ces nouvelles approches favorisent l'inclusion financière et l'accès au crédit des emprunteurs les plus fragiles. Cependant, le Machine Learning appliqué à ces données peut aussi conduire à des biais et à des phénomènes de discrimination. |
| Keywords: | Machine Learning ML,Credit scoring,New data,Nouvelles données,Scoring de crédit,Apprentissage automatique |
| Date: | 2019–11–21 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-02377886&r=all |
| By: | Simplice A. Asongu (Yaoundé/Cameroon); Nicholas M. Odhiambo (Pretoria, South Africa) |
| Abstract: | This study provides thresholds of inequality that should not be exceeded if gender inclusive education is to enhance gender inclusive formal economic participation in sub-Saharan Africa. The empirical evidence is based on the Generalised Method of Moments and data from 42 countries during the period 2004-2014. The following findings are established. First, inclusive tertiary education unconditionally promotes gender economic inclusion while the interaction between tertiary education and inequality is unfavourable to gender economic inclusion. Second, a Gini coefficient that nullifies the positive incidence of inclusive tertiary education on female labour force participation is 0.562. Second, the Gini coefficient and the Palma ratio that crowd-out the negative unconditional effects of inclusive tertiary education on female unemployment are 0.547 and 6.118, respectively. Third, a 0.578 Gini coefficient, a 0.680 Atkinson index and a 6.557 Palma ratio are critical masses that wipe-out the positive unconditional effects of inclusive tertiary education on female employment. Findings associated with lower levels of education are not significant. As the main policy implication, income inequality should not be tolerated above the established thresholds in order for gender inclusive education to promote gender inclusive formal economic participation. Other implications are discussed in the light of Sustainable Development Goals. This research complements the existing literature by providing inequality thresholds that should not be exceeded in order for gender inclusive education to promote the involvement of women in the formal economic sector. |
| Keywords: | Africa; Inequality; Gender; Inclusive development |
| JEL: | G20 I10 I32 O40 O55 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:exs:wpaper:19/087&r=all |