Im currently writing my bachler's thesis about economic growth (GDP per capita) and democracy (Polity), and some other independent variables like Economic Freedom, Life expectancy, mean years of schooling and investments.

Background:
I have done tests to control for heteroskedasticity, autocorrelation, stationarity and multicollinearity.

The data is heteroskedastic (to counter this, robust standard errors are used, which in Stata 16.1 is clustered)
No autocorrelation
One of the variables was non-stationarity (Economic Freedom) and was remade in first difference which solved to issue of non-stationarity.
No multicollinearity (according to results from VIF)


I use panel data with a panel a strongly balanced panel (because I interpolate missing values).
t = 23
n = 147
observations = 3381

I conducted a hausman test and realized that the fixed model is prefered for this panel.

To the problem and analysis:
When I conduct my panel data analysis, robust command is used (xtreg variable 1, variable 2 etc, fe robust)
Array



All the results are insignificant and the R2-value is very low as you can see. I am concerned about this since I was expecting a higher R2-value since many of the independent variables is directly linked to GDP per capita.
I also know that insignificance is not a problem since it is also a result that can be interpreted as "there is no connection between these variables". But since the R2 is so low I am afraid that there is something wrong with the entire analysis.

I also ran a miss-specification test to see if I the model was miss-specificed, the null hypothesis was rejected indicating no problem of miss-specification.

If there is no problem here, could you please give an example of an article that explains that low R2 is not a problem.
And if there is a problem, what do you think the problem is? Many previous studies on the subject have used the same type of variables without this problem.
If there is any uncertainties, feel free to ask me.

PS. I know of the dataex command, but I think this is a better visual representation.