2021
DOI: 10.1080/15228916.2021.1930810
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The Relationship between Financial Inclusion and Monetary Policy: A Comparative Study of Countries’ in Sub-Saharan Africa and Latin America and the Caribbean

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Cited by 24 publications
(44 citation statements)
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“…To measure financial inclusion, we use the principal component analysis (PCA) technique to include aspects related to geographic and demographic penetration and to represent the supply and demand-side dimensions of financial products and services, specifically, access and use of financial products and services. This complies with the suggestions of other scientific studies that substantiate that using a single variable to measure financial inclusion such as bank account ownership, access to bank credit, total bank branch, ATM per adult or Km2, bank deposit and savings may produce biased results and no practical application (Feghali et al 2021;Jungo et al 2021;Pham et al 2019;Sarma 2008). Additionally, the construction of the financial inclusion index prevented us from using all financial inclusion indicators simultaneously in the estimation, avoiding the multicollinearity problem (Batuo et al 2018).…”
Section: Data Descriptionsupporting
confidence: 87%
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“…To measure financial inclusion, we use the principal component analysis (PCA) technique to include aspects related to geographic and demographic penetration and to represent the supply and demand-side dimensions of financial products and services, specifically, access and use of financial products and services. This complies with the suggestions of other scientific studies that substantiate that using a single variable to measure financial inclusion such as bank account ownership, access to bank credit, total bank branch, ATM per adult or Km2, bank deposit and savings may produce biased results and no practical application (Feghali et al 2021;Jungo et al 2021;Pham et al 2019;Sarma 2008). Additionally, the construction of the financial inclusion index prevented us from using all financial inclusion indicators simultaneously in the estimation, avoiding the multicollinearity problem (Batuo et al 2018).…”
Section: Data Descriptionsupporting
confidence: 87%
“…Respecting the multidimensionality of financial inclusion, we aggregate the dimensions of the supply and demand side of financial products and services. According to Carillo et al (2019), Carvalho (2013), Jungo et al (2021), andSarma (2008) the construction technique of the financial inclusion index is specified as in Equation (1).…”
Section: Model Specificationmentioning
confidence: 99%
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