Calculations with year variable

[Yanina Goeminne]

Dear all

I have the following sample dataset.
For each firm, I have the operating revenue turnover for the year 2014-2017.
I also made seperate operating revenue turnover variables.
Now, I want to calculate the revenue growth for for example 2014-2015.
I actually want to do the following:
operatingrevenueturnover2015 - operatingrevenueturnover2014 / operatingrevenueturnover2014
If I do it this way, this does not work.
How can I do this?

Thanks!

* Example generated by -dataex-. To install: ssc install dataex
clear
input float firm int year double operatingrevenueturnover float(operatingrevenueturnover2014 operatingrevenueturnover2015 operatingrevenueturnover2016 operatingrevenueturnover2017)
 1 2017    750000         .         .         .    750000
 1 2015    713500         .    713500         .         .
 1 2016    751700         .         .    751700         .
 1 2014    783781    783781         .         .         .
 2 2016    621000         .         .    621000         .
 2 2015    621000         .    621000         .         .
 2 2014    579000    579000         .         .         .
 2 2017    600000         .         .         .    600000
 3 2014  84146095  84146096         .         .         .
 3 2016 108837094         .         . 108837096         .
 3 2015  91007681         .  91007680         .         .
 3 2017 116326189         .         .         . 116326192
 4 2017    252000         .         .         .    252000
 4 2015    252000         .    252000         .         .
 4 2014    225000    225000         .         .         .
 4 2016    252000         .         .    252000         .
 5 2014    454000    454000         .         .         .
 5 2016    653000         .         .    653000         .
 5 2017    490000         .         .         .    490000
 5 2015    490000         .    490000         .         .
 6 2014  62731632  62731632         .         .         .
 6 2016  64443465         .         .  64443464         .
 6 2017  70734286         .         .         .  70734288
 6 2015  62652350         .  62652352         .         .
 7 2015   7000000         .   7000000         .         .
 7 2014  11511100  11511100         .         .         .
 7 2017   8000000         .         .         .   8000000
 7 2016   7500000         .         .   7500000         .
 8 2014   1000000   1000000         .         .         .
 8 2017   1450000         .         .         .   1450000
 8 2016   1300000         .         .   1300000         .
 8 2015   1200000         .   1200000         .         .
 9 2015   2510000         .   2510000         .         .
 9 2016   2340000         .         .   2340000         .
 9 2017   2300000         .         .         .   2300000
 9 2014   3021000   3021000         .         .         .
10 2017   1700000         .         .         .   1700000
10 2016   1751000         .         .   1751000         .
10 2014   1751000   1751000         .         .         .
10 2015   1751000         .   1751000         .         .
11 2017    120000         .         .         .    120000
11 2015   7000000         .   7000000         .         .
11 2016     90000         .         .     90000         .
11 2014  10500000  1.05e+07         .         .         .
12 2014   1543400   1543400         .         .         .
12 2017   1543500         .         .         .   1543500
12 2016   1736000         .         .   1736000         .
12 2015   1929000         .   1929000         .         .
13 2016   1152000         .         .   1152000         .
13 2017   1200000         .         .         .   1200000
13 2015   1152000         .   1152000         .         .
13 2014   1152000   1152000         .         .         .
14 2014   2800000   2800000         .         .         .
14 2016   2704900         .         .   2704900         .
14 2017   3010000         .         .         .   3010000
14 2015   2300000         .   2300000         .         .
15 2017   1800000         .         .         .   1800000
15 2015   1400000         .   1400000         .         .
15 2014   1800000   1800000         .         .         .
15 2016   1500000         .         .   1500000         .
16 2014   2020000   2020000         .         .         .
16 2017   2700000         .         .         .   2700000
16 2015   2020000         .   2020000         .         .
16 2016   2020000         .         .   2020000         .
17 2017     84000         .         .         .     84000
17 2015     93948         .     93948         .         .
17 2014     90876     90876         .         .         .
17 2016     74072         .         .     74072         .
18 2016   2947300         .         .   2947300         .
18 2014   1528000   1528000         .         .         .
18 2017   3647233         .         .         .   3647233
18 2015   2533800         .   2533800         .         .
19 2016   6444000         .         .   6444000         .
19 2015   5463000         .   5463000         .         .
19 2017   6444000         .         .         .   6444000
19 2014   6724000   6724000         .         .         .
20 2014    293000    293000         .         .         .
20 2017    609000         .         .         .    609000
20 2016    305000         .         .    305000         .
20 2015    304000         .    304000         .         .
21 2017 292546298         .         .         . 292546304
21 2014 330467701 330467712         .         .         .
21 2016 292327723         .         . 292327712         .
21 2015 315647667         . 315647680         .         .
22 2015   5561719         .   5561719         .         .
22 2016   6278254         .         .   6278254         .
22 2014   6294419   6294419         .         .         .
22 2017   8196184         .         .         .   8196184
23 2016   2300000         .         .   2300000         .
23 2017   2800000         .         .         .   2800000
23 2014   2000000   2000000         .         .         .
23 2015   2100000         .   2100000         .         .
24 2016    730000         .         .    730000         .
24 2017    730000         .         .         .    730000
24 2015    730000         .    730000         .         .
24 2014    730000    730000         .         .         .
25 2017    421000         .         .         .    421000
25 2015    420000         .    420000         .         .
25 2014    420000    420000         .         .         .
25 2016    420000         .         .    420000         .
end

[William Lisowski]

The code you showed was

operatingrevenueturnover2015 - operatingrevenueturnover2014 / operatingrevenueturnover2014

In Stata, as in mathematics in general, multiplication and division take precence over addition and subtraction. You need

( operatingrevenueturnover2015 - operatingrevenueturnover2014 ) / operatingrevenueturnover2014

to cause Stata to calculate the difference between the 2015 and 2014 values and then divide that difference by the 2014 value.

But that's not the full problem. Here's how you need to do this.

* Example generated by -dataex-. For more info, type help dataex
clear
input float firm int year double operatingrevenueturnover
 1 2017    750000
 1 2015    713500
 1 2016    751700
 1 2014    783781
 2 2016    621000
 2 2015    621000
 2 2014    579000
 2 2017    600000
 3 2014  84146095
 3 2016 108837094
 3 2015  91007681
 3 2017 116326189
 4 2017    252000
 4 2015    252000
 4 2014    225000
 4 2016    252000
 5 2014    454000
 5 2016    653000
 5 2017    490000
 5 2015    490000
 6 2014  62731632
 6 2016  64443465
 6 2017  70734286
 6 2015  62652350
 7 2015   7000000
 7 2014  11511100
 7 2017   8000000
 7 2016   7500000
 8 2014   1000000
 8 2017   1450000
 8 2016   1300000
 8 2015   1200000
 9 2015   2510000
 9 2016   2340000
 9 2017   2300000
 9 2014   3021000
10 2017   1700000
10 2016   1751000
10 2014   1751000
10 2015   1751000
11 2017    120000
11 2015   7000000
11 2016     90000
11 2014  10500000
12 2014   1543400
12 2017   1543500
12 2016   1736000
12 2015   1929000
13 2016   1152000
13 2017   1200000
13 2015   1152000
13 2014   1152000
14 2014   2800000
14 2016   2704900
14 2017   3010000
14 2015   2300000
15 2017   1800000
15 2015   1400000
15 2014   1800000
15 2016   1500000
16 2014   2020000
16 2017   2700000
16 2015   2020000
16 2016   2020000
17 2017     84000
17 2015     93948
17 2014     90876
17 2016     74072
18 2016   2947300
18 2014   1528000
18 2017   3647233
18 2015   2533800
19 2016   6444000
19 2015   5463000
19 2017   6444000
19 2014   6724000
20 2014    293000
20 2017    609000
20 2016    305000
20 2015    304000
21 2017 292546298
21 2014 330467701
21 2016 292327723
21 2015 315647667
22 2015   5561719
22 2016   6278254
22 2014   6294419
22 2017   8196184
23 2016   2300000
23 2017   2800000
23 2014   2000000
23 2015   2100000
24 2016    730000
24 2017    730000
24 2015    730000
24 2014    730000
25 2017    421000
25 2015    420000
25 2014    420000
25 2016    420000
end

by firm (year), sort: generate revenuegrowth = ( operatingrevenueturnover - operatingrevenueturnover[_n-1] ) / operatingrevenueturnover[_n-1]
format %9.4f revenuegrowth
format %12.0fc operatingrevenueturnover
list if firm<=5, abbreviate(30) sepby(firm)

. list if firm<=5, abbreviate(30) sepby(firm)

     +--------------------------------------------------------+
     | firm   year   operatingrevenueturnover   revenuegrowth |
     |--------------------------------------------------------|
  1. |    1   2014                    783,781               . |
  2. |    1   2015                    713,500         -0.0897 |
  3. |    1   2016                    751,700          0.0535 |
  4. |    1   2017                    750,000         -0.0023 |
     |--------------------------------------------------------|
  5. |    2   2014                    579,000               . |
  6. |    2   2015                    621,000          0.0725 |
  7. |    2   2016                    621,000          0.0000 |
  8. |    2   2017                    600,000         -0.0338 |
     |--------------------------------------------------------|
  9. |    3   2014                 84,146,095               . |
 10. |    3   2015                 91,007,681          0.0815 |
 11. |    3   2016                108,837,094          0.1959 |
 12. |    3   2017                116,326,189          0.0688 |
     |--------------------------------------------------------|
 13. |    4   2014                    225,000               . |
 14. |    4   2015                    252,000          0.1200 |
 15. |    4   2016                    252,000          0.0000 |
 16. |    4   2017                    252,000          0.0000 |
     |--------------------------------------------------------|
 17. |    5   2014                    454,000               . |
 18. |    5   2015                    490,000          0.0793 |
 19. |    5   2016                    653,000          0.3327 |
 20. |    5   2017                    490,000         -0.2496 |
     +--------------------------------------------------------+