Hi all. I have a panel dataset from Brazilian states with information on the number of radio stations per state (n_radio_stations).

What I need: n_radio_stations needs to be normalized by the area of the state (area_km2) to give the number of radio stations per square miles. Then this variable needs to be normalized by each state’s population (i_pop_total) to give the number of radio stations per unit of area per population.

This variable will provide an index of the intensity of the radio infrastructure in each state. I’m confused about how to do this calculation. Can someone help?

The aforementioned data follows below.

ibge_uf_code = state’s ID


Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input int(year ibge_uf_code n_radio_stations) long area_km2 double i_pop_total
1970 11   2  243044   111064
1970 12   4  152589   215299
1970 13   9 1558987   955203
1970 14   1  230104    40885
1970 15  12 1227530  2166998
1970 16   4  139068   114230
1970 17   .  286944   521139
1970 21  10  324616  2992678
1970 22   9  250934  1680573
1970 23  26  146817  4361603
1970 24  12   53015  1550184
1970 25  11   56372  2382463
1970 26  26   98306  5161866
1970 27   9   27652  1588068
1970 28   6   21994   900679
1970 29  34  559921  7493437
1970 31 121  582586 11485663
1970 32  11   45597  1599324
1970 33  34   43305  8994802
1970 35 262  247320 17770975
1970 41  98  199060  6929821
1970 42  62   95495  2901660
1970 43 123  267527  6664841
1970 50   .  350548   998160
1970 51  19  881001   598849
1970 52  33  355092  2416890
1970 53  10    5771   537492
1974 11   3  243044   263048
1974 12   7  152589   249690
1974 13  12 1558987  1145333
1974 14   1  230104    56179
1974 15  13 1227530  2661598
1974 16   4  139068   138641
1974 17   .  286944   608303
1974 21  10  324616  3394184
1974 22   9  250934  1864022
1974 23  24  146817  4732333
1974 24  12   53015  1689644
1974 25  11   56372  2537616
1974 26  29   98306  5554521
1974 27   9   27652  1746007
1974 28   7   21994   996559
1974 29  32  559921  8278219
1974 31 125  582586 12243440
1974 32  10   45597  1768930
1974 33  63   43305  9913534
1974 35 240  247320 20679415
1974 41 103  199060  7209832
1974 42  63   95495  3192313
1974 43 125  267527  7108444
1974 50   .  350548  1146804
1974 51  19  881001   814877
1974 52  33  355092  2698584
1974 53  13    5771   793258
1978 11   6  243044   415033
1978 12   .  152589   284081
1978 13  14 1558987  1335463
1978 14   2  230104    71474
1978 15  14 1227530  3156198
1978 16   3  139068   163052
1978 17   .  286944   695467
1978 21  11  324616  3795691
1978 22  10  250934  2047471
1978 23  31  146817  5103064
1978 24  13   53015  1829105
1978 25  15   56372  2692769
1978 26  30   98306  5947176
1978 27  11   27652  1903946
1978 28   8   21994  1092439
1978 29  40  559921  9063001
1978 31 134  582586 13001217
1978 32  12   45597  1938535
1978 33  72   43305 10832265
1978 35 281  247320 23587854
1978 41 137  199060  7489843
1978 42  77   95495  3482966
1978 43 149  267527  7552047
1978 50   .  350548  1295447
1978 51  30  881001  1030904
1978 52  39  355092  2980278
1978 53  10    5771  1049025
1982 11  10  243044   607692
1982 12   6  152589   322447
1982 13  20 1558987  1552840
1982 14   5  230104   104296
1982 15  22 1227530  3684691
1982 16   3  139068   196011
1982 17   .  286944   771924
1982 21  16  324616  4166227
1982 22  15  250934  2219731
1982 23  49  146817  5484469
1982 24  15   53015  1992786
1982 25  21   56372  2848667
1982 26  41   98306  6322476
1982 27  10   27742  2079494
1982 28  10   21994  1204288
1982 29  52  559951  9894046
1982 31 155  582586 13809750
1982 32  18   45597  2128298
1982 33  88   43305 11567281
end