Is there a certain method to communicate the results of earnings without logging the variable? 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.


In my experience with econometric data, I always log monetary values (income, wealth, rent and so on). But, so far I only published in a more sociological venues and not econometric ones so I am not sure if that is a common practice in econometrics. The reason for the log is that in my experience (not with European countries data) these numbers are log normally distributed. You can check that quickly by doing a Q-Q plot for the log income.
Thus, if I am right regarding your data, you should take the log of the income. And there is an added bonus as you mentioned. The currency conversions are a multiplicative constant (if the rate is constant during the period) which will be incorporated into the Intercept using the log income. There, problem solved - all coefficient of the regression are comparable.
If the exchange rate is not constant for 2000 and 2018, then I think you should convert the local currencies to a single one, and then take the log (if the data distribution is log-normal)
