Income and Wealth Inequality in Malta

Abstract

This paper studies for the first time the distribution of income and wealth in Malta based on three waves of micro-level data from the Household Finance and Consumption Survey (HFCS). The focus of the analysis is to examine various socioeconomic aspects of income and wealth inequality and contributing factors, as well as determinants of the joint distribution of income and wealth. Results suggest that household main residence (HMR) is the most equalising factor of wealth inequality, while self-employment wealth is the most disequalising factor. The financing structure of HMR is particularly important to explain the position of a household in the wealth distribution. Furthermore, the age and education level of the reference person of a household, increases in household income and inheritance or gifts affect positively the probability of a household to be in a higher net wealth quintile.

Examining the impact of personal freedom on income inequality: Evidence from Sub-Saharan Africa and Western European regions

“…The unconditional quantile regression (UQR) model was employed for household expenditure and income surveys (HEIS, 2017/2018), and the results showed that the education of household heads is a crucial driver of income inequality in Jordan, as well as the geographical location of households. [ 55 ] used microdata from the Household Finance and Consumption Survey (HFCS) from 2010 to 2016 to study income and wealth disparities in Malta. The study employed decomposition methods and binary response models and found that income and wealth distribution changed in favour of households in the upper parts of the distributions and persons with tertiary education.…”

“…Fourth , as argued by [ 49 , 52 , 53 ], synthetic income inequality measures such as the Gini coefficient are unable to accurately account for the structure of inequality, especially for heavy-tailed distributions [ 54 ] hence, call for the need to go yonder. To help better understand the structure of persistent income inequality in Africa, this study employs the income share of the top 10%, which has been described as key in explaining overall inequality [ 55 ], as a proxy for income inequality. Finally , the literature on income inequality has mostly examined the relationship with other economic variables by employing a conditional mean model with a fixed effect as seen in studies such as; [ 22 , 29 , 56 ]; however, this study goes beyond by employing a panel quantile regression estimator [ 57 ] to test for non-linearity and understand the heterogeneity in the effect of personal freedom on income inequality at different quantiles.…”