Can Digital Financial Inclusion Promote Women’s Labor Force Participation? Microlevel Evidence from Africa

Our study analyzes the relationship between digital financial inclusion and women’s labor force participation, as well as shedding light on the barriers to women’s digital financial inclusion. We have mobilized a microeconomic database that covers 15,192 African women. Our database is extracted from the Global Findex database, 2021 edition, based on nationally representative surveys of 29 African countries. The Probit model estimation methodology is used to examine the empirical results. Our findings reveal that financial inclusion via the digital channel is positively associated with women’s labor force participation more than the traditional channel. A significant and positive impact of formal financial services channels on the level of women’s participation in the labor market was uncovered. Our research has shown that women face a variety of obstacles when it comes to accessing financial services, both through traditional channels and digital means. These barriers include nonvoluntary obstacles in traditional financial inclusion channels. However, as a woman’s income level increases, the intensity of these barriers decreases. When it comes to digital financial inclusion, women often face a unique set of obstacles, such as the high cost of mobile financial services, lack of money, and lack of access to a cellphone. The study contributes to the existing literature by investigating the impact of digital financial inclusion on women’s labor force participation in African countries and identifying barriers that hinder women’s digital financial inclusion based on individual-level data. It suggests that African policymakers should increase women’s financial inclusion through digital channels to improve their participation in the labor market

Feature Selection for Multiclass Support Vector Machines. Computer & Communication, AI Communications Vol.29, n°5, DOI 10.3233/AIC-160707 IOS Press, ISSN 0921-7126, pp.583–593.

International audienc

Variables Selection for Multiclass SVM Using the Multiclass Radius Margin Bound

International audienceSupport vector machines (SVM) are considered as a powerful tool for classification which demonstrate great performances in various fields. Presented for the first time for binary problems, SVMs have been extended in several ways to multiclass case with good results in practice. However, the existence of noise or redundant variables can reduce their performances, where the need for a selection of variables. In this work, we are interested in determining the relevant explanatory variables for an SVM model in the case of multiclass discrimination (MSVM). The criterion proposed here consist in determining such variables using one of the upper bounds of generalization error specific to MSVM models known as radius margin bound [1]. A score derived from this bound will establish the order of relevance of variables, then, the selection of optimal subset will be done using forward method. The experiments are conducted on simulated and real data, and some results are compared with those of other methods of variable selection by MSVM

Feature selection for multiclass support vector machines

International audienceIn this paper, we present and evaluate a novel method for feature selection for Multiclass Support Vector Machines (MSVM). It consists in determining the relevant features using an upper bound of generalization error proper to the multiclass case called the multiclass radius margin bound. A score derived from this bound will rank the variables in order of relevance, then, forward method will be used to select the optimal subset. The experiments are firstly conducted on simulated data to test the ability of the score to give the correct order of relevance of variables and the ability of the proposed method to find the subset giving a better error rate than the case where all features are used. Afterward, four real datasets publicly available will be used and the results will be compared with those of other methods of variable selection by MSVM

Variables Selection for Multiclass SVM Using the Multiclass Radius Margin Bound

International audienceSupport vector machines (SVM) are considered as a powerful tool for classification which demonstrate great performances in various fields. Presented for the first time for binary problems, SVMs have been extended in several ways to multiclass case with good results in practice. However, the existence of noise or redundant variables can reduce their performances, where the need for a selection of variables. In this work, we are interested in determining the relevant explanatory variables for an SVM model in the case of multiclass discrimination (MSVM). The criterion proposed here consist in determining such variables using one of the upper bounds of generalization error specific to MSVM models known as radius margin bound [1]. A score derived from this bound will establish the order of relevance of variables, then, the selection of optimal subset will be done using forward method. The experiments are conducted on simulated and real data, and some results are compared with those of other methods of variable selection by MSVM