Hi all,
I am trying to implement the overall correction for unequal income group stated in Clarke, P., & Van Ourti, T. (2010). Calculating the concentration index when income is grouped. Journal of Health Economics, 29(1), 151-157.
I am having trouble doing this. In particular- 1) How do we calculate fractional rank based on each group in Stata? I understand the formula, however, I am not comfortable generating a code in Stata for the same
2) Is there a Stata package that already has taken this into account? 3) Since we cannot compute the measurement error, do we ignore that in the formula?
For your convenience I am trying to evaluate equation 8 :

i is the individual
n is the total number of indiviudals
m is the health care variable
nj the number of individuals in group j.
Rk is the fractional rank for income in group k
epsilon is the error term
delta is the measurement error
Any help will be appreciated.
Thanks and Kind Regards,
Aarushi
I am trying to implement the overall correction for unequal income group stated in Clarke, P., & Van Ourti, T. (2010). Calculating the concentration index when income is grouped. Journal of Health Economics, 29(1), 151-157.
I am having trouble doing this. In particular- 1) How do we calculate fractional rank based on each group in Stata? I understand the formula, however, I am not comfortable generating a code in Stata for the same
2) Is there a Stata package that already has taken this into account? 3) Since we cannot compute the measurement error, do we ignore that in the formula?
For your convenience I am trying to evaluate equation 8 :
i is the individual
n is the total number of indiviudals
m is the health care variable
nj the number of individuals in group j.
Rk is the fractional rank for income in group k
epsilon is the error term
delta is the measurement error
Any help will be appreciated.
Thanks and Kind Regards,
Aarushi
Comment