2. Literature Review
Income Inequality Measures: ncome inequality in the paper refers to “economic inequality” between groups in population. There are many methods the measure inequality in the literature. Popular measure of income inequality is the Gini coefficients. This ranges the income inequality for 0 to 1, which 0 is the perfect equality and 1 is the perfect income inequality. Other measures are Theils T and Theils L, which allow decomposing the income inequality into parts such as rural and urban areas Atkinson’s class of income inequality is more general and it sometime be used (Haughton & Khandker, 2009). With the about measures of income inequality have the advantage that they do know to show the effecting factors to the income inequality level. In this paper, the authors will discuss more on the regression-based approach to explain the factors of income inequality by employing development policy review and an econometric regression model.
Regression-based Approach: Income inequality can be measured by the differences of income or expenditure per capita. It is linked with skill, education, opportunities, happiness, health, life expectancy, welfare, assets and social mobility (Heshmati, 2004). The studies of income inequality have been conducted for more than the past three decades. This section examines the progress of income inequality on household studies, with special attention given to income inequality indices and regression-based composition approach. Shorrocks (1980, 1982) decomposed income inequality by income sources and population subgroups.
e mentioned “the quantitative significance of income variations associated with age, sex, race, occupation, the level of education, and so on”. He e argued that income is contributed by different sources, and that income inequality can be analyzed by the variances of these sources. Oaxaca (1973) and Blinder (1973) developed the regression-based method for measuring income inequality.
They used this method to measure inequality of wage in labor economics. They employed variables known as “individual characteristics” in their regression, and separated their models into two groups to quantify the inequality of wage income. Their model tried to make an explanation for the reason of “whites earn much higher wages than blacks and males earn substantially higher wages than females” “Discrimination coefficient” was mentioned by both of the authors to explain why the differential wagesexist in case of other “characteristics” do not change (Blinder, 1973; Oaxaca, 1973), and (Adger, 1999).
Fields & Yoo (2000) and Fields (2003) further developed the method by using income generating equation to “account for” or “decompose” inequality in a country and its change over time. Gunderson ((1989) identified the discrimination of wages between male and female. In defining the gap of wages, he proposed some methods such as narrowing defined occupation and regression wage decomposition. He applied the regression that breaks down wage differentials by the difference of “characteristics” and “structure” between male and female. Pracharopoulos and Patrinos (1994) attempted to identify the ethnic discrimination in Latin American countries where almost all of the populations are indigenous peoples. They used multivariate regression analysis method popularized by Oaxaca (1973) and Blinder (1973). Their study concluded that indigenous people are poor, illiterate, and prone to health problems and disadvantage in earning. They also explained that less education is strongly correlated to poverty.
Interestingly, their statistical results showed that much of earnings differential between indigenous and non-indigenous workers would disappear by equalizing human capital characteristics. They finally proposed that a further research should “combine the quantitative approach taken with qualitative analysis, such as the participatory-observation research approach (or participatory poverty assessment)”. Without this qualitative data, probable reasons for the discrepancy, including race, access to training, and
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