Households’ Population Data Disaggregation: A Financial Perspective

Abstract

Background/Objectives: This study seeks to contribute to the ongoing debate on the relevance of a well-structured national population statistics and the achievement of financial inclusion which is necessary for financial stability, economic growth and development. Methods/Statistical analysis: Financial inclusion measured from the demand side serves as the framework for this study. Data was sourced from the Global Findex (2017). A multivariate tobit model is specified and the maximum likelihood is employed for estimation. From the financial inclusion and social inclusion data, financial index and social index were computed following guides from the literature. Findings: Our study reveals that financial inclusion has positive and significant effects for both the business and non-business households. However, factors like; age, location and some levels of income gave varying effects for the business and non-business households. For the business households, rural factor predicts social outcomes positively and significantly by 23%. The reverse is the case for non-business households where it predicts social outcome negatively and significantly by 21%. For the business households, the age factor predicts social outcome positively and significantly by 3%, whereas for the non-business households, it predicts negatively and significantly by 18%. At quintile 1, income predicts social outcomes for business households negatively and significantly by 14% whereas, for non-business households, it predicts positively and significantly by 10%. Our findings provide further justification for the separation of household data into business and non-business for policy effectiveness. Novelty/Applications: this study is one of the pioneer works in household population data disaggregation in the dimension of finance which separates the effect of financial inclusion for business and non-business households in Nigeria.