If I want to estimate a linear probability model with (region) fixed effects, is that the same as just running a fixed effects regression? Maybe I'm getting tripped up with the language and whether I should be using the lm or plm R packages. My goal is to estimate the effect of a baby bonus. My dependent variable is a binary indicator for NEWBORN and my main independent variable of interest is an indicator for receiving the baby bonus. I control for age, age squared, education, marital status, and household income.
1) ## Linear Probability LPM <- lm(newborn ~ treatment + age + age_sq + highest_education + marital_stat + hh_income_log, data = fertility_15_45) #how do I add FE to a lm model in R? 2) ## FE Model FE_model <- plm(newborn ~ treatment + age + age_sq + highest_education + marital_stat + hh_income_log, data = fertility_15_45, index="region", model="within")