UM E-Theses Collection (澳門大學電子學位論文庫)
- Title
-
Economic development and income inequality in Macau
- English Abstract
-
Show / Hidden
Accompanying with the rapid development of economy in Macau, the problem of income inequality becomes more serious. Hence, it is necessary to measure the degree of income inequality in order to give hints to government for implementing related policies for the purpose of mitigating this socioeconomic problem. In this paper, Gini coefficient is the measurement of income inequality and we can obtain this estimation consistently and precisely by finding the best fit income distribution model first. Four income distribution models will be introduced and applied for Macau, and they are the lognormal distribution, log-logistic model, Singh-Maddala model and the Dagum model. After the calculation and computation, we find that the results of Singh-Maddala model fit the best for Macau’s income distribution, and we have used these results to compute the Gini coefficient. Moreover, we also do the factors analysis of income inequality for Macau though OLS regression and find that the relationship between economic growth and income inequality follows the Kuznets inverted U-shaped hypothesis with statistically significant, and their relationship is bilateral causality. In addition, the unemployment rate and the female employment respectively have significant positive and negative relationship with income inequality. Furthermore, another important factor causing income inequality is economic structural change that it has a significant positive effect. Finally, some of the suggestions of policies have been given to the government for the purpose of improving income inequality in Macau.
- Issue date
-
2014.
- Author
-
Seng, Oi Ian
- Faculty
-
Faculty of Social Sciences
- Department
-
Department of Economics
- Degree
-
M.Soc.Sc.
- Subject
-
Economic development -- Macau
Income distribution -- Macau
Macau -- Economic conditions
- Supervisor
-
Ho, Wai Hong
- Files In This Item
- Location
- 1/F Zone C
- Library URL
- 991006733419706306