How to interpret fixed effects? I want to interpret the output of a fixed effects regression and need help with interpreting the country-fixed effects.
The regression is the following:
pm.alldata &lt;- pdata.frame(alldata , index = c(&quot;country&quot;, &quot;year&quot;) )
a.fixedtwo &lt;- plm(log(production) ~ log(temp) + log(rain) + drought + flood + storm + log(labour) + log(fertilizer) +log(capital) +log(area) , data = pm.alldata, model = &quot;within&quot;, effect = &quot;twoways&quot;)


The dependent variable is agricultural production. I want to look at how temperature and precipitation affect agricultural production (although this is rather irrelevant to the question I have). The country fixed effects refer to 28 countries. The county-fixed effects are as follows:
As I understand it, we can say that country 5 (Ecuador) has an unobservable negative effect (-5,99469) on agricultural production. Am I right?
Now I come to my main question:
I have divided these 28 countries into two subgroups (poor and rich countries).
If I regress only the 14 poor countries, the coefficients of the country-fixed effects change to the following:

Now the effect of country 5 (Ecuador) is suddenly positive (7.5768). This would mean that Ecuador has positive unobservable effects on agricultural production. Is it normal for the signs to change when this is subdivided into a subgroup?Which of the two values of Ecuador should I use for interpretation when comparing the value of Ecuador with the value of a rich country (e.g. Argentina)?
Fixed effects model is estimated as:
$$   y_{i t} − \bar{y_i} = ( X_{i t} − \bar{X_i} ) \beta + ( \alpha_i − \bar{\alpha_i} ) + ( u_{it} − \bar{u_i} )$$
So the country fixed effect is always relative to the average fixed effect.
If a country has negative fixed effect that means it is less productive than average country in your sample. If you choose different sample results might change.