dineq (version 0.1.0)

dineq_rb: Regression-based decomposition of inequality

Description

Decomposition of (income) inequality into multiple characteristics. A regression-based decomposition method is used.

Usage

dineq_rb(formula, weights = NULL, data)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted in the ordinary least squares regression.

weights

an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. Should be inside selected data frame in the function and between quotation marks.

data

a data frame containing the variables in the model.

Value

a list with the results of the decomposition, containing the following components:

inequality_measures

the values of 4 inequality measures: gini, mean log deviation, theil and variance of log income

decomposition_inequality

the (relative) decomposition of the inequality into the different variables

regression_results

results of the ols regression which is used to make the decomposition of inequality

note

number of zero or negative observations. The function uses a logarithmic transformation of x as input for the regression. Therefore these observations are deleted from the analysis

Details

This function uses a multivariate regression-based decomposition method. Multiple variables can be added to the function in order to calculate the contribution of each individual variable (including the residual) to the inequality. For instance socio-economic, demographic and geographic characteristics (such as age, household composition, gender, region, education) of the household or the individual can be added.

This decomposition can be used on a broad range of inequality measure, like Gini, Theil, mean log deviation, Atkinson index and variance of log income.

It uses a logarithmic transformation of the values of the dependent variable. Therefore it cannot handle negative or zero values. Those are excluded from the computation in this function.

The main difference with the decomposition of the mean log deviation or Gini coefficient is that multiple characteristics can be analyzed at the same time. While the other decomposition functions only analyze one characteristic at the same time.

References

Fields, G. S. (2003). <U+2018>Accounting for income inequality and its change: a new method, with application to the distribution of earnings in the United States<U+2019>, Research in Labor Economics, 22, p. 1<U+2013>38.

Brewer M., and L. Wren-Lewis (2016) Accounting for Changes in Income Inequality: Decomposition Analyses for the UK, 1978<U+2013>2008. Oxford Bulletin of economics and statistics, 78 (3), p. 289-322,

See Also

dineq_change_rb

Examples

# NOT RUN {
#Decomposition of the income inequality into 4 variables using Mexican Income data set:
data(mex_inc_2008)
inequality_decomp <- dineq_rb(income~hh_structure+education+domicile_size+age_cat,
weights="factor", data=mex_inc_2008)

#selection of the output: decomposition of the inequality into the contribution of the
#different variables and residual (adds up to 100 percent)
inequality_decomp["decomposition_inequality"]

# }