The Human Development Index as a Predictor of Financial Inclusion Level, a cross-Countries analysis

Résumé

The purpose of this research is to confirm the relationship between the financial inclusion index and the human development index and attempt to forecast it using the latter based on a worldwide sample constituted of countries with various economics and social levels.

The correlations between these important dimensions have been demonstrated in earlier studies using a local sample. Due to the unique characteristics of each sample, this could result in a biased study. We reasoned that the study could not be applied to other contexts because employing a different sample would result in a different outcome. However, a research study's findings based on a particular sample may be wonderful and positive in some environments but unfavorable in others.

the used approach consists to collect secondary data of 49 countries worldwide, with various economic and social levels, to compute the financial inclusion index from international monetary fund’s data set, and for Human development index, we gathered its data from Human development report database. We used deductive reasoning and the kernels test, a non-parametric test, to confirm this connection.

By using a cross-country analysis, this research demonstrated the existence of the relationship between the human development index and the level of financial inclusion, and it demonstrated that we can use the human development index to predict the level of financial inclusion by demonstrating a positive correlation, which confirm the previous assumptions. The human development index, however, was able to account for about 26% of the variation in the financial inclusion index, according to R square. that maintain a low percentage.

As a conclusion, the relationship between the Human Development Index and the degree of financial inclusion has always been a problem for researchers, which calls for a particular examination. The analysis gap entails examining the association between these factors using local samples from each nation, which may produce biased results due to the nation's unique characteristics.

To demonstrate that there is a real connection and that we might rely on this relationship to forecast the level of financial inclusion, we examined this relationship using a global sample made up of numerous nations with varying economic and social levels. Although our predictive model's power is still untapped because only 26% of the variance in the level of financial inclusion in our particular case can be explained to the human development index, this study revealed a relationship between these two variables, validating earlier findings.