Using Propensity Score Matching to select similar firms

[EJAA]

Hi All

Thanks in advance for the help to solve my problem; 

My problem is outlined below;

From Column 2 in Table 1 shows firms that are linked to a specific firm. So for instance, firm with id 101 is linked to firm with id 102,104 and 103 while firm with id number 102 is linked with two firms thus 104 and 101.

Table 1: Interconnected firms

ID	Firmid1	Firmid2	Firmid3	Firmid4
101	102	104	103
102	104	101
103	101	103	104
104	104	1002	101

Table 2 indicates firm characteristics (size,roa)

date	ID	size	Roa
2010	101	6	12
2011	101	2	14
2012	101	5	13
2010	102	2	19
2011	102	4	14
2012	102	2	12
2010	103	3	13
2011	103	2	16
2012	103	1	19
2010	104	6	9
2011	104	7	8
2012	104	8	10

What I want to do is that, for the list of firms connected to a specific firm in table 1, I want to use propensity score matching to match firms that have similar characteristics and are connected using size and roa from table 2. So for instance from table 1, firm with id 101 is linked to 3 firms thus 102,104 and 103. Hence from the three firms linked to 101, I want to use propensity score matching to establish which of the three firms have similar features with 101 using size, roa.

Thanks for the help. 

[Ksharp]

The best choice is use SAS/OR to get this kind fo connection component.

After link all these ID up, it is easy to get what you want.

data x;
input ID Firmid1 Firmid2 Firmid3 Firmid4;
cards;
101 102 104 103 .
102 104 101 . .
103 101 103 104 .
104 104 1002 101 .
;
run;
data have;
 set x;
 array x{*} Firmid1-Firmid4;
 from=id;
 do i=1 to dim(x);
   if not missing(x{i}) then do;to=x{i};output;end;
 end;
 keep from to;
run;


data full;
  set have end=last;
  if _n_ eq 1 then do;
   declare hash h();
    h.definekey('node');
     h.definedata('node');
     h.definedone();
  end;
  output;
  node=from; h.replace();
  from=to; to=node;
  output;
  node=from; h.replace();
  if last then h.output(dataset:'node');
  drop node;
run;


data want(keep=node household);
declare hash ha(ordered:'a');
declare hiter hi('ha');
ha.definekey('count');
ha.definedata('last');
ha.definedone();
declare hash _ha(hashexp: 20);
_ha.definekey('key');
_ha.definedone();

if 0 then set full;
declare hash from_to(dataset:'full(where=(from is not missing and to is not missing))',hashexp:20,multidata:'y');
 from_to.definekey('from');
 from_to.definedata('to');
 from_to.definedone();

if 0 then set node;
declare hash no(dataset:'node');
declare hiter hi_no('no');
 no.definekey('node');
 no.definedata('node');
 no.definedone();
 

do while(hi_no.next()=0);
 household+1; output;
 count=1;
 key=node;_ha.add();
 last=node;ha.add();
 rc=hi.first();
 do while(rc=0);
   from=last;rx=from_to.find();
   do while(rx=0);
     key=to;ry=_ha.check();
      if ry ne 0 then do;
       node=to;output;rr=no.remove(key:node);
       key=to;_ha.add();
       count+1;
       last=to;ha.add();
      end;
      rx=from_to.find_next();
   end;
   rc=hi.next();
end;
ha.clear();_ha.clear();
end;
stop;
run;

[EJAA]

Dear KSharp

Thanks very much for the great help. Sorry for late update to your reply on my problem.

I have followed the codes closely  but the final output which is dataset want contains only two columns thus node and household.

Please at what point do I use table 2 as expounded in my problem in your codes. Thanks very much.

[Rick_SAS]

Check out PROC PSMATCH in SAS/STAT, which performs propensity score matching.