alternative for clustered standard errors when having too few clusters

Hey everyone,

I have observations on management scores from firms which are nested in countries. This means that observations are clustered.
In order to account for this clustering I first thought of using clustered standard errors. Unfortunately I only have 18 countries and therefore only 18 clusters which means that using clustered standard errors would cause small sample bias.
What is an alternative to use in this case??

I also thought about using a fixed effects model in order to account for the onobserverd heterogeneity, but as my key explanatory variable only varies across countries and not across firms this is not possible.
So my second question would be if I should do -xtreg,re- or simply -reg- ?

My model looks as follows: My key explanatory variable is PDI which only varies across countries and not over firms.

Management_ij = a + b₁ * PDI_j + b₂ * x_ij + e_ij

Thanks for any help in advance!!

Best,
Hanna

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