Hidden Demographics Barriers of the Economic Growth: A Psychometric Approach

Abstract

This paper examined the hidden demographic barriers of economic growth. The study used a cross-sectional survey researches design. The primary data were collected by using a psychometric scale from 211 individuals who were randomly sampled from the Mwanza and Kagera regions in Tanzania. The data were linearly analysed by the weighted least squares (WLS) and Analysis weighted- automatic linear modelling (AW-ALM), and non-linearly analysed by Gaussian mixture model (GMM) and neural network analysis (NNA). The study found that the main hidden demographic barrier to economic growth is the negative subjective well-being of an individual’s current age and education level. Moreover, the GMM revealed that there is no significant data or regional clusters or classes in the study population. Furthermore, NNA evidenced the most effective predictor of economic growth is age, followed by education. The study concluded that the most hidden demographic factors that hinder economic growth are negative perceptions of an individual on his/her current age and level of education, not the age maturity, and education level. Operationally or practically, the paper implicates several socio-economical policies, mostly the national aging policy (NAP), the National Education and Training policy (NETP), the National Employment Policy (NEP), and regulations /laws on national social security funds schemes at national, regional and global levels. Therefore, the paper recommended that government and other education stakeholders increase the policy commitment on the mathematics, science, and technology subjects to be compulsory for primary and secondary schools, and the extension of the retirement age from 60 years (voluntary) to 65 years (compulsory)

Homo-Hetero Pairing Effect Correlation Coefficient

“…The psychometric scale technique was used to process the data as suggested by Bundala (2022), which is suitable for cross-sectional studies. The data is primarily analysed by using , and the results are compared with that of the r and regression model.…”

Human Development and Governance in Africa

“…The first section pertains to connections among development indicators as well as determinants of human development. These include Bundala (2012) on the connection between economic growth and the human development index and Eren and others (2014) who have investigated the relationships among constituents of human development indicators. Matekenya and others (2020) and Asongu and Nting (2022) have examined the relationship between financial inclusion and human development while Asongu and Odhiambo (2021) have investigated how social media influences human development.…”