My research questions are:
- Does the gap in earnings has changed between individuals with different educational levels between 2007 and 2010 (before and after the 2008 financial crisis).
- Does that change, if founded, is stronger among countries that were severely hit by the economic crisis compared with countries less touched?
I am analysing two cross-sectional data, one in 2007 and another in 2010. I am aware of the limitations of comparing two cross-sectional data across time. My question is regarding executing the model, as two different strategies give me different results.
The first method i ran a regression for each country alone such as:
lm(earnings ~ education*year + age + gender, data=df)
year is a binary variable (2007 and 2010). The interaction
education*year shows whether the gap in earnings increased or decreased between individuals with a different educational background in each country separately.
2)The second method i pool all countries together and introduce a dummy variable for countries. Then, i interpret a three-way interaction between education, countries and year.
lm(earnings ~ education*country*year + age + gender, data=df)
The two different analytical strategies yield somewhat different results. The results of the 2nd method also change depending on which country i specify as a reference category. My question is regarding which method seems more appropriate to my research questions after taking into consideration all the limitation of the cross-sectional data. Multilevel design is not possible as i have only six countries.