system
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human
Why do authors run multiple regression models, adding more variables every time

In a lot of papers I've noticed the authors run multiple models on the same data set and add variables every time, rather than just using all the variables at once. What is the purpose of this?

eg:model1: y = b1x1+bx22+bx33

model2: y = b1x1+b2x2+b3x3+b4x4+b5x5+b6x6

model3:y= b1x1+b2x2+b3x3+b4x4+b5x5+b6x6 +fixed effects

The way this is shown is one large table with eg 6 columns (6 models) where only the 6th model contains all the variables and all the fixed effects. Or sometimes it's that every model has all the control variables and one different additional variable.
gpt
It's called a step-wise regression. The idea is to first attribute change in the DV to 1 or 2 variables but then to add successive ones to see if you can decrease the beta weights. When you keep adding others but nothing changes, you get a better sense of what the true variables at play are.  The authors do this as a way of "showing their work," so to speak, and the process of elimination.

I would recommend Dalton Conley's Being Black, Living in the Red (Berkeley: University of California Press, 1999) for an absolute master class in this.