Good Afternoon, everyone.

I would like to ask about choosing the right model when we use data of population.

Currently I'm working on my research, about tourism sector and economic growth by using sample of 33 out of 34 provinces in Indonesia, from 2012-2017. The model can be constructed as:

Y = f (X1, X2, X3, X4, e)

Y is economic growth, X1 is number of workforce, X2 is realization of foreign direct investment & domestic direct investment, X3 is education level of workforce, and X4 is tourism sector, proxied by tax of hotel and restaurant.

The hypothesis used here is one-tailed positive, so I only consider the sign of coefficient from each variable: as long it has positive coefficient, the hypothesis is proved.

Then I choose the right model. As we know, in general, there are 3 model we can use to analyze panel data: common effect, fixed effect, and random effect. Then I run 3 test, that is Chow, LM and Hausman. From those test, the result is random effect is preferred than the other two.

At the presentation of my research in front of panel judges, one of them said that because I use the data of population (33 of 34 provinces in Indonesia), I do not have to run 3 test (Chow, LM, and Hausman). He said that I should use fixed effect instead. But he still want me to check whether his statement is correct or not.

I try to read some econometric books, but I still can not find the statement that say "if you use the data of population, then you have to use fixed effect". Because of that reason, I come here, that maybe I can find some enlightment here.

Thanks for your advance.

Sincerely,
Alifan.