If you have SAS/ETS then proc expand is the way to go (method=join added in subsequent edit) :
data have2;
set have;
date=mdy(12,31,year);
format date date9.;
run;
proc expand data=have2 out=new from=year method=join;
by fips;
id date;
convert assn nccs pvote respn sc_score std_scscore;
run;
If you don't then a judicious use of lags and arrays will work. The program below assumes that you have data which uniformly has exactly the same missing pattern for all six (I did not see seven) variables. So if ASSN is missing, so are the other 5, and vice versa.
The technique here is to alway have available the curent (in array CURD) and preceding data (array LAGD). The progam also assumes that you are only interpolating and never extrapolating (i.e. you always have the first year and last year not missig).
data want (drop=cury lagy v coef);
set have ;
where assn^=.;
by fips;
array d{*} assn nccs pvote respn sc_score std_scscore ;
array curd{6} _temporary_;
array lagd{6} _temporary_;
cury=year;
lagy=lag(year);
do v=1 to dim(d);
curd{v}=d{v};
lagd{v}=lag(curd{v});
end;
if first.fips then output;
else do year=lagy+1 to cury;
coef=(year-lagy)/(cury-lagy);
do v=1 to dim(d);
d{v}=lagd{v} + coef*(curd{v}-lagd{v});
end;
output;
end;
run;
