Determinants of Gini coefficient as indicator of income inequality in the Philippines

• Main Authors: Robang, Robert I., Reposar, Gerrymaine, Romero, Karina Clara C., Romero, Ronald P., Baylon, Dioscoro P., Jr.
• Format: text
• Published: Animo Repository 2019
• Subjects: Income distribution—Philippines; Gini coefficient; Income distribution—Mathematical models; Economics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/9188
• Institution: De La Salle University

The Philippines as a country in the Asia Pacific that has a growing economy but at the same time experiences rising income inequality. According to the World Bank, the Gini Index of the Philippines from 2010 to 2015 fluctuated from 40.10-42.20%. Data shows the poorest 20% of the population only had a share of 4.45% of the national income, while the richest 20% contributed nine times, indicating extreme disparity in the country’s wealth distribution.This study aimed to determine whether Employment, Mortality Rate, Population Growth, and Poverty, as determinants have a significant relationship to the Gini Coefficient, and to determine the relationship between Gini coefficient and Income inequality. Multiple regression was used in interpreting relationships between variables.Findings indicated that three variables have a significant relationship with Gini coefficient, i.e., employment rate and mortality rate. Based on coefficient results, employment, population growth rate and poverty incidence in the Philippines are positive, implies a direct relationship with the Gini coefficient. On the contrary, Mortality rate showed an indirect relationship.Mortality rate, the most significant factor of Gini coefficient, implies it is the biggest contribution to income inequality measurement in the Philippines, indicating that as mortality rate rises, deaths mostly in the low-bracket income, decrease income inequality. The employment rate is the second significant variable with a direct relationship with Gini suggesting that if employment rate rises, income of the high bracket continues to grow faster than the low-bracket. Poverty and Population variables have no significance to Gini coefficient. Thomas et.al. (2000) study supports this finding that population size becomes not relevant to Gini especially in a large population country.