Hi, it is on an urgent basis

I am running the following regression: with and without mean group averages in a diff-in-diff cross-sectional data set up.

* Example generated by -dataex-. To install: ssc install dataex clear input float(id year_of_birth years_of_education Post_X_policy) str1 states float(sex_ratio literacy_rate Current_age Household_asset Male_head survey_year) 1 1985 10 1 "A" .43 36 35 1 1 2000 2 1986 11 0 "B" .55 21 31 0 1 2005 3 1987 15 1 "A" .46 26 30 1 1 2000 4 1985 17 1 "C" .15 29 25 0 0 2000 5 1991 15 1 "B" .36 24 26 1 1 2005 6 1991 14 1 "A" .66 21 30 0 1 2000 7 1993 15 0 "B" .55 23 22 1 1 2000 8 1985 20 0 "C" .74 55 27 1 0 2005 end reg years_of_education Post_X_policy sex_ratio literacy_rate Current_age Household_asset Male_head i.states i.year_of_birth , cluster(states) When I am just doing vce robust I am getting an F test but with clustering my F-test goes missing. Now my worry of concern is the small clustering group. My dataset is large but the no of clusters i.e at the state level is low (around 12 or less, whereas minimum 42clustering group is the rule of thumb) How should I deal with this ? I am aware of Cameroon's wild bootstrap score approch but as the F test with clustering at state level is missing. I am afraid. Given the scenario, should I implement bootstrap clustering rather than going for a state level clustering followed by wild bootstrap score??