# My independent variable of interest changes sign and becomes statistically significant when I add year and country fixed effects to the model

I have a dataset that covers loan applications from the year 2012 to 2017. It also includes information on the borrower (e.g., age, income, employment status) and information on the loan (loan duration, loan amount, interest rate).

I have a regression where the main independent variable of interest is a gender dummy (1 = female) and the dependent variable is a loan interest rate. I run two specifications of this model. In the first I include, besides the gender dummy, a set of borrower- and loan-level control variables. The second model is identical to the first one, but here I add year and country fixed effects.

In the first model, the coefficient of the gender dummy is negative and statistically insignificant. In the second model, the coefficient of the gender dummy now becomes positive and statistically significant. Can someone help my grasp why the sign of the coefficient flips when adding the year and country fixed effects to the model and why it becomes significant? Or what kind of further testing or analysis I can do to figure this out myself?

• Welcome to Cross Validated! If you are testing a null hypothesis of no effect (this is typical, so I feel confident in assuming you are), getting an insignificant effect means that your positive point estimate lacks the precision to call it a significantly positive value. In other words, it might be positive, zero, or negative. Thus, it should not really be so shocking that a “significant” result has a different sign.
– Dave
Commented Aug 19, 2023 at 11:39
• Thanks! I am indeed testing a null hypothesis of no effect. One thing I don't yet quite understand is how in what way the inclusion of the fixed effects contribute to the sign becoming positive instead of negative? Commented Aug 19, 2023 at 11:49
• Commented Aug 19, 2023 at 14:14