I am investigating whether earnings differences have widened between different social classes in several European countries by comparing two different periods. The picture below shows the findings of an interaction, here in the code:
lm(income ~ class + age + head_sex + household_size + period*class, data = df)
The base category of the variable class is: upper-middle class. The interaction
period*class shows whether the gap between any of the three shown social classes have widened relative to the
upper-middle class between two different periods. The variable
period is a binary variable (e.g., 2000 vs 2018).
The issue in this graph is that these countries have different currencies (dollar,euro, pound, zloty). This makes it difficult for the reader to make sense a bit of the results. For example, 389 zlotty is around $87, but if the reader is not familiar with that currency, they might not understand how large or small is this effect. Two possible solutions that i know could help with that but i do not want to use them for several reasons that i will not discuss here. First, to log the
income variable. Second, to convert all currencies to one that is standardized across them, for example using the
dollar as common currency.
I was wondering whether anyone has any advice on other solutions which i can use to communicate these results in a more intuitive way?
Is dividing these estimates by the median earnings of one period or the average of two periods would make sense? that way we might have a better feeling of how large are these differences within one country, but at the same time will be comparable across countries.
Any help would be greatly appreciated.