st: RE: creating variable summarizing for each individual properties of other members of a group at t-1

[Nick Cox <n.j.cox@durham.ac.uk>]

I don't know what "I am familiar with" means here. Does it mean that you've read the FAQ but can't see how to apply it? 

This sounds to me like 

1. Get the sum of all individual entries 

sort yearmonth 
gen score = sum(ind_entry) 
by yearmonth : replace score = score[_N] 

2. Subtract this individual 

replace score = score - ind_entry 

3. Look one step back in time 

bysort ind_id (yearmonth) : gen prevscore = score[_n-1] 

Nick 
n.j.cox@durham.ac.uk 

Erik Aadland
 
I need to create a variable that sums for each individual in my dataset the total number of ind_entry of all other individuals at time: yearmonth - 1.
I have attached a small ex of my data structure below. So for instance, given the small dataset below, for ind_id 2 in yearmonth 11 this variable score = 1. But for ind_id 4 in the same yearmonth, the score = 0.
 
I would also like to generate a variable that identifies for each individual the unique number of other individuals in the dataset that have experienced ind_entry = 1 at least once up until time: yearmonth - 1.
 
I am familiar with the following FAQ: http://www.stata.com/support/faqs/data/members.html
 
My data structure is snapshot data in principle like the example below, but some individuals enter the observation window later than others (i.e. in later yearmonths):
 
year    month    yearmonth    ind_id    ind_entry
2003   10         10               2          0
2003   11         11               2          0
2003   12         12               2          0
2004   1          13               2          0
2004   2          14               2          1
2004   3          15               2          0
2003   10         10               4          1
2003   11         11               4          0
2003   12         12               4          1
2004   1          13               4          0
2004   2          14               4          0
2004   3          15               4          0
 

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