There is a rich literature on the methods and techniques that focusingon measuring the income inequality. We found that four Different methods havebeen developed to decompose income inequality in the literature.
The firstmethod have been developed by Mincer (1958 and 1970) Becker (1964) and Oxaaca(1973) which concerns with estimatingthe differences in the means. The second method deals with decomposinginequality within-subgroups and between-groups ingredients (Pyatt, 1976; Shorrocks,1984; Fields, 2000; Almeida dos Reis and de Barros; 1991, Cowell and Jenkins1995; and Morduch and Sicular, 2002). The third method concerns with applyinglinear regression approach on income functions ( Morduch and Sicular; 2002, Fields;2003, and Rani et al.
; 2017). The fourth method is based on the factoringredients ( Fei et al. 1978; Pyatt et al. 1980, and Shorrocks 1982). Inequality is decomposed by subgroups, income source anddemographic sources. Fields (2000) and Morduch and Sicular (2002) useregression-based decomposition method.
The proportional contribution of anincome source in inequality is the coefficient of that source from theregression equation of income multiplied by a factor of adjusted weighted meanof income by sources to mean income. Standard errors of source contributionsare also computed. Fields (2003) propose different method which is applicableto the changes in income inequality, and it is useful any index of inequality.
Heused standard semi-logarithmic regression model of income in order to obtainthe contributions of different indicators to the change and the level ininequality. The benefit from his approach that it can integrate several indicatortypes in the regression model. Moreover it can include nonlinear effects and controllingfor endogeneity. Rani et al. (2017) used Fields (2003) and Bigotta etal. (2015) to decompose income inequality in India.
The primary points ofinterest of this method is the likelihood of including quantitative and also qualitativevariables as contributing factors and its possibility to apply to the measuresof inequality. The Gini coefficient is the most widely measure of inequality.Giles (2004) estimates the Gini coefficient and its standard deviations usinglinear regression, while the assumption of the ordinary least square method areviolated. Davidson (2009) used an asymptotic approach primarily based on thenatural estimator of the cumulative distribution. Lubrano and Ndoye (2016)showed that the distribution of log-normal is suitable for modeling thedistribution of income. They decompose inequality within-subgroups andbetween-groups ingredients by applying a finite mixture of log-normaldistributions using Bayesian methods and Gibbs sampling. Moreover, the strongprior information indicates that Bayesian standard deviations of the Ginicoefficient are somewhat low compared with those found in Davidson (2009) andmuch lower than those found in Giles (2004).Inequality can also be decomposed at different levels.
At thenational level, it can be decomposed into within-subgroup and between-subgroupcomponents (Heshmati, 2004).Statistical inference on inequality and its dimensions is proposedby Van de gaer, Funnell and McCarthy (1999),where they name two ways of inference with measures of inequality or of functionsof inequality: Bootstrapped standard errors or large-sample distributions’standard errors. Biewen, M. (2002) showed that abootstrapping simulation and provided an evidence that this method is betterthan asymptotic methods.