I am currently doing the analysis using firm-level data of three countries combining together. Also, it is a cross-sectional analysis. Therefore, in the Ordered Probit regression, I control for industry dummies to account for differential effects in the industry level.

I think I don't need to control for country dummies due to some reasons: (1) The countries that I use in my analysis share similar characteristics including development level, political, and socio-economic conditions. (2) There are only three countries in my analysis.

In this case, I expect that the results will not be affected even though I don't control for country specifics.

However, I am not sure if my idea is appropriate since I cannot find any reference to support it yet. Therefore, I would like to ask everybody here if you have any idea whether or not country dummies is necessary in my case.

Thank you!

  • $\begingroup$ Without knowing the exact context of the model it is hard to give a full answer; But I don't think the examples you give to not include country level variables are very compelling: (1) the key is in the use of similar, eg. they're still not identical, and so therefore might have nuanced differences, (2) there are many models that use binary variables (eg. sex in a medical trial) so three countries doesn't seem small. You may wish to fit the model using multi-level/hierarchical modelling if you expect the countries behaviours to be similar. $\endgroup$ – owen88 May 20 at 6:18
  • $\begingroup$ @owen88 My objective is to study a causal relation that is independent of country. So, I think that including country dummies will be the right thing to do. However, I got a problem when I include those country variables, that is the parallel assumption for ordered probit regression is violated. So, I am now trying to figure out any possible solution. I would also appreciate it if you have some suggestions, like to run the regression for each country separately or other possible ways $\endgroup$ – Vannaro May 21 at 5:45

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