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on Financial Literacy and Education |
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Issue of 2019‒06‒17
two papers chosen by |
| By: | Asongu, Simplice A; Odhiambo, Nicholas M |
| Abstract: | This study investigates how enhancing gender inclusion affects inequality in 42 African countries for the period 2004-2014. The empirical evidence is based on the Generalized Method of Moments. Three inequality indicators are used, namely, the: Gini coefficient, Atkinson index, and Palma ratio. The two gender inclusion measurements used include female labour force participation and female employment. The following main findings are established. There are positive net effects on inequality from the enhancement of gender inclusion dynamics. An extended threshold analysis is used to assess critical masses at which further increasing gender inclusion enhances inequality. The established thresholds are: (i) 55.555 ???employment to population ratio, 15+, female (%)???for the nexus with the Gini coefficient. (ii) 50 ???labor force participation rate, female (% of female population ages 15+)??? and between 50 to 55 ???employment to population ratio, 15+, female (%)???, for the Atkinson index. (iii) 61.87 ???labor force participation rate, female (% of female population ages 15+)??? for the Palma ratio. These established thresholds are worthwhile for sustainable development because, beyond the critical masses, policy makers should complement the gender inclusion policy with other measures designed to reduce income inequality. Some complementary measures that can be taken on board beyond the established thresholds could focus on enhancing, inter alia: information and communication technology, infrastructural development; financial inclusion and inclusive education. |
| Keywords: | Africa; Gender; Inclusive development; Sustainable development |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:uza:wpaper:25512&r=all |
| By: | Majid Bazarbash |
| Abstract: | Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating. |
| Date: | 2019–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/109&r=all |