0

I am running a regression where I am trying to identify the influence of the proximity to Tuskegee, Macon County and the percentage of African American population on the Covid-19 vaccination rate on county level.

reg vaccination_perc_points c.dist_Tuskegee##c.perc_points_afri_am i.urban_rual i.education_low_high median_income perc_points_male social_capital_index perc_points_am_indian_alaka_native perc_points_asian, robust

Now I would like to apply state effects. I am wondering whether I should apply random or fixed effects. I control for all possible realizations of states which would indicate fixed effects. But I am only interested in a fraction of my population (African American) which favors, as far as I unterstood, random effects.

I would very much appreciate your advice regarding fixed or random effects.

Karolis Koncevičius
  • 4,282
  • 7
  • 30
  • 47
Anna Losacker
  • 1
  • I would have thought random. Otherwise you are estimating 50 parameters in which you have no interest. – mdewey Nov 27 '21 at 13:42

1 Answers1

0

Individual attributes are usually modeled as random effects.

Random effects involve pooling from other categories, assuming the samples from each category come from the same higher level population and are somewhat similar. It works well even when each category has small samples, cause it uses data in other categories as well.

See: What is the difference between fixed effect, random effect and mixed effect models?

Pik-Mai Hui
  • 389
  • 7