See text: https://phantran.net/random-effects-regression-in-panel-data-analysis-using-stata/
Database: https://drive.google.com/file/d/1G3NF-jL6Eoz9zrOjad5dMZrv33-Sp_D2/view?usp=sharing
Data in excel: https://docs.google.com/spreadsheets/d/135p2zph7SL6I5Y5UyCu7eKFzcOitrBLb/edit?usp=sharing&ouid=100029919331612689631&rtpof=true&sd=true
The rationale behind random effects model is that: the variation across entities is assumed to be random and uncorrelated with the independent variables included in the model. So, “…the crucial distinction between fixed and random effects is whether the unobserved individual effect embodies elements that are correlated with the regressors in the model, not whether these effects are stochastic or not” (Green, 2008, p.183). Random effects assume that the entity’s error term is not correlated with the independent variables which allow for time-invariant variables to play a role as independent variables. If we have reason to believe that differences across entities have some influence on our dependent variable, then we should use random effects. An advantage of random effects is that: we can include time invariant variables (such as superficies of province). In the fixed effects model, these variables are absorbed by the intercept.