# Sample selection and variation in the variable of interest when using panel data

I wonder how to reason when selecting samples. I am doing a panel data regression analysis about how the euro membership correlates with the budget deficit in the member countriues.

Using R that is:

BUDGETDEFICIT ~ EURODUMMY + some_controll_variables


EURODUMMY is the variable of interest. Using panel data I have anual observations for many countriies. My question is how selecting what sample to use effects the regression analysis.

Should I only use countries that has introduced the Euro?

Is adding other European countries or western like countries benefitial as they add more datapoints and/or acts like a reference gropup?

Or is id only bad for the data to add countries that has not introduced the euro as they have no variation in the variable of interest (EURODUMMY)?

Other comments regarding this problem is also welcome.

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Model and data source

As in any other applications you should start from the plausible model, that would fit most of the countries in your cross-sectional dimension. Suppose you do have a quite universal theory that fits most of the countries in the world. Then you could try to include as many countries as possible using either World Bank, IFS statistics or simply Penn World table data. However you may also limit yourself to more homogeneous samples like EU data taken from Eurostat or OECD statistics.

Cross-section of countries

Since in panel data models (you may also consider linear mixed effects models as an option, since you also include interaction terms) under you (mostly) quantitative hypothesis "was there a significant (both statistically and economically) impact of belonging to euro-zone club on the budget deficit?" you have to balance the number of countries that are in the euro-zone, adopted it from a certain year, and are out of the club.

Should I only use countries that have introduced the Euro?

Is adding other European countries or western like countries beneficial as they add more data points and/or acts like a reference group?

Regarding these particular questions, in my opinion, the inclusion of EU countries and some OECD rivals to France, Germany and Italy, would be sufficient for your analysis. You have not to limit only to euro-zone club, because of selection bias. Well some countries are adopted euro within the time dimension, but it is better to compare with the countries that are not so restricting their fiscal policy due to strict Maastricht criteria they obliged to follow.

Or is id only bad for the data to add countries that has not introduced the euro as they have no variation in the variable of interest (EURODUMMY)?

Dummy is a variable that has two values $0$ and $1$, so if you include only (not in this case though) $1$ you will face pure multicollinearity problem with the common intercept term, that is present by default in plm model.

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 Thank you very much for the answer. I wonder about the last one about adding no varying dummy variables. If I add only present members of the EURO-area those dummies will still vary since I use data from the years before they introduced the euro. But for countries that hasn't introduced the dummy will have a 0 for the the whole period. – Skolnick Jan 20 '11 at 11:12 @Skolnick: but if you don't your results could be biased, you would like to know the impact of being in the club as compared to the countries not in the club (hence you need more out of the club countries to be confident enough). If you have a particular question if the entrance into club has had an impact, than introduce one more dummy for the countries that were not originally in euro-zone. – Dmitrij Celov Jan 20 '11 at 12:15