Different Ways of Calculating Gini Coefficient Using Different Types of Data
Different types of data can be used to calculate the index. Depending on the type of data used, Gini coefficient only provides a specific and narrow perspective of inequality.
15 May 2016
To begin with, let’s look at how wage inequality is measured.
Currently, the most widely used measurement for inequality is the Gini coefficient. One of the problems with looking at inequality using Gini coefficient is that different types of data can be used to calculate the index. Depending on the type of data used, Gini coefficient only provides a specific and narrow perspective of inequality.
The figure below, for example, shows six types of data that can use to calculate the index.
At the individual level, inequality can first be looked at narrowly in terms of only personal wages using earnings from hourly (Step 1), monthly (Step 2) and annual (Step 3) works.
Next, the value of the index also depends on whether part-time or full-time wage rates are used. The importance of this factor increases with the extent of dispersion of working time. The greater the proportion of part-time workers within an economy, the more important this factor becomes in determining the value of the index.
Another factor, the employment intensity, is whether those who are unemployed all or part of the year are included in the calculation of Gini. It has been shown in the EU that the biggest jump takes place between steps 2 and 3 when their inclusion increases the value of Gini coefficient significantly by 30% on average.
To lower the value of Gini coefficient so as to make growth looks more inclusive than it really is, policymakers may choose to exclude part-time workers or workers who are not fully employed from the dataset.
Inequality can also be looked at in terms of overall income. For example, incomes from capital can be added to labour earnings (Step 3) to arrive at the total personal market income (Step 4) which can then be pooled together as a family unit to get household market income (Step 5). In general, this pooling of resources helps to reduce the value Gini coefficient. Finally, to get the household disposable income, adjustments such as taxes and transfers are made (Step 6).
The experience of European countries shows that level of inequality can change quite substantially when different wage or income figures are used to calculate Gini coefficient.
Correlations between Wage and Income Inequality in Europe
Overall, there appears to be no fixed pattern of correlation between wage inequality and income inequality. The levels of wage and income inequality vary from country to country across Europe. The figure below shows the correlation between the Gini index for full-time wages and for disposable household income in 2011. The dashed horizontal and vertical lines indicate the EU’s average of wage and income inequality.
As can be seen from the chart, countries such as the UK and Portugal have high levels of both wage and income inequality. Luxembourg, Germany and Austria, on the other hand, have high levels of wage inequality but low levels of income inequality. Countries such as Finland, Sweden, Denmark, and Belgium have low levels of both wage and income inequality. Finally, countries such as Spain, Greece, France, and Italy have low levels of wage inequality but high levels of income inequality.
In general, the classification seems to be roughly associated with the different European social models:
the first with the Anglo-Saxon or Liberal model;
the second with a Germanic variant of the continental model;
the third with the Nordic model and the most social-democratic continental countries; and
the fourth with the Mediterranean model.
In short, the European experience shows that when looking at Gini coefficient, it is important to know the data use for its calculation and to remember that it only tells a narrow aspect of inequality.
One way to see a more complete picture, for example, is to look at Gini coefficient in conjunction with a host of other indicators and ratios that can even be obtained from the same data set used to calculate the coefficient, but dissected in different ways.
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 See Eurofound. (2015). “Recent developments in the distribution of Wages in Europe.”
 See Eurofound. (2015).