Picking initial values for a specific

[Hugo Rocha]

Dear all,

I have an unbalanced panel of 57,910 observations of country, industries and year (2003-2018). And I would like to pick values of value added per worker in 2005. Nevertheless, when I do this on my panel I get that my coefficients are omitted for collinearity. My data now has no duplicates. So, my issue is that perhaps I am not using the right sintax to get the initial values in 2005. Any advice? Thank you very much!



* Example generated by -dataex-. To install: ssc install dataex
clear

 input int(country year isic1) float(country_industry TotalValueAdded logval_worker tradability_output tradability_country)
 8 2010 1010  1    96789712  8.800137   .1768752 .08869135
 8 2011 1010  1   125793104   8.74405  .15556923  .0863666
 8 2012 1010  1   176615488  8.695241   .1463699 .09472585
 8 2013 1010  1   131003208  9.109263    .164018 .14183685
 8 2014 1010  1   163158896  8.896569  .14443323 .13885614
 8 2015 1010  1   123891064  8.491784  .13452409 .12520152
 8 2016 1010  1   149982240  8.632782   .1765093 .11871612
 8 2017 1010  1   193711168  8.778877   .1774767 .14302167
 8 2018 1010  1   225976368  8.933749   .1989122 .14653176
 8 2010 1030  2    96789712  8.862595    .215029 .08869135
 8 2011 1030  2   125793104  8.762699  .21581225  .0863666
 8 2012 1030  2   176615488  8.792838  .20265244 .09472585
 8 2013 1030  2   131003208  8.798987  .20287557 .14183685
 8 2014 1030  2   163158896   9.24072  .19782813 .13885614
 8 2015 1030  2   123891064  8.477221  .20720184 .12520152
 8 2016 1030  2   149982240  8.561826  .27469403 .11871612
 8 2017 1030  2   193711168  8.420509  .26632798 .14302167
 8 2018 1030  2   225976368  8.732944  .26901108 .14653176
 8 2010 1040  3    96789712  10.06337   .3355838 .08869135
 8 2011 1040  3   125793104  9.008738  .19421376  .0863666
 8 2012 1040  3   176615488  9.875433   .2092457 .09472585
 8 2013 1040  3   131003208  9.586471  .17979157 .14183685
 8 2014 1040  3   163158896   9.45741  .17748743 .13885614
 8 2015 1040  3   123891064  8.265269   .1502877 .12520152
 8 2016 1040  3   149982240  8.601744  .24313246 .11871612
 8 2017 1040  3   193711168   8.94572  .24658133 .14302167
 8 2018 1040  3   225976368  9.221366  .27100095 .14653176
 8 2010 1050  4    96789712  9.241973  .12098086 .08869135
 8 2011 1050  4   125793104  9.180054  .11871654  .0863666
 8 2012 1050  4   176615488   8.78597   .1152698 .09472585
 8 2013 1050  4   131003208  8.432419  .14063852 .14183685
 8 2014 1050  4   163158896  8.729236  .11514153 .13885614
 8 2015 1050  4   123891064  8.724345  .11546322 .12520152
 8 2016 1050  4   149982240   8.84583  .13292608 .11871612
 8 2017 1050  4   193711168  8.945738  .14246953 .14302167
 8 2018 1050  4   225976368  9.014386   .1586218 .14653176
 8 2010 1410  5    96789712  8.425086   .2258532 .08869135
 8 2011 1410  5   125793104  8.540539    .388695  .0863666
 8 2012 1410  5   176615488  8.541057   .3685393 .09472585
 8 2013 1410  5   131003208    8.6199   .4107426 .14183685
 8 2014 1410  5   163158896  8.638366   .4123004 .13885614
 8 2015 1410  5   123891064  8.292302   .4005206 .12520152
 8 2016 1410  5   149982240  8.294022   .3923832 .11871612
 8 2017 1410  5   193711168  8.441474   .4230156 .14302167
 8 2018 1410  5   225976368  8.549866    .440155 .14653176
 8 2011 1910  6   125793104  9.159176  .14866973  .0863666
 8 2012 1910  6   176615488 10.614596   .1523459 .09472585
 8 2014 1910  6   163158896   8.74985  .17065017 .13885614
 8 2015 1910  6   123891064   7.74456  .16286217 .12520152
 8 2016 1910  6   149982240  7.694532   .1238466 .11871612
 8 2017 1910  6   193711168  10.03203  .13627516 .14302167
 8 2018 1910  6   225976368 10.130532  .13455296 .14653176
 8 2010 3100  7    96789712  8.986774   .2543435 .08869135
 8 2011 3100  7   125793104  9.029225   .3122739  .0863666
 8 2012 3100  7   176615488   9.05458  .31815135 .09472585
 8 2013 3100  7   131003208  9.016403  .29301417 .14183685
 8 2014 3100  7   163158896  9.176045  .28281972 .13885614
 8 2015 3100  7   123891064  8.728418  .28148833 .12520152
 8 2016 3100  7   149982240  8.708357   .2639583 .11871612
 8 2017 3100  7   193711168  8.844979   .2826954 .14302167
 8 2018 3100  7   225976368  8.803402  .31619525 .14653176
12 2011 1010  8 75196186624         .  .15556923 .12106317
12 2012 1010  8 70800785408         .   .1463699 .13205482
12 2013 1010  8 66283483136         .    .164018 .13740344
12 2014 1010  8 61726912512         .  .14443323  .1576208
12 2015 1010  8 34376491008         .  .13452409 .11134155
12 2011 1020  9 75196186624         .   .3592764 .12106317
12 2012 1020  9 70800785408         .  .34193125 .13205482
12 2013 1020  9 66283483136         .  .29112867 .13740344
12 2014 1020  9 61726912512         .  .29428324  .1576208
12 2015 1020  9 34376491008         .  .27611375 .11134155
12 2011 1030 10 75196186624         .  .21581225 .12106317
12 2012 1030 10 70800785408         .  .20265244 .13205482
12 2013 1030 10 66283483136         .  .20287557 .13740344
12 2014 1030 10 61726912512         .  .19782813  .1576208
12 2015 1030 10 34376491008         .  .20720184 .11134155
12 2011 1040 11 75196186624         .  .19421376 .12106317
12 2012 1040 11 70800785408         .   .2092457 .13205482
12 2013 1040 11 66283483136         .  .17979157 .13740344
12 2014 1040 11 61726912512         .  .17748743  .1576208
12 2015 1040 11 34376491008         .   .1502877 .11134155
12 2011 1050 12 75196186624         .  .11871654 .12106317
12 2012 1050 12 70800785408         .   .1152698 .13205482
12 2013 1050 12 66283483136         .  .14063852 .13740344
12 2014 1050 12 61726912512         .  .11514153  .1576208
12 2015 1050 12 34376491008         .  .11546322 .11134155
12 2011 1072 13 75196186624         .  .13218853 .12106317
12 2012 1072 13 70800785408         .  .12412757 .13205482
12 2013 1072 13 66283483136         .   .1235223 .13740344
12 2014 1072 13 61726912512         .  .11877646  .1576208
12 2015 1072 13 34376491008         .  .11726826 .11134155
12 2011 1080 14 75196186624         .  .06792859 .12106317
12 2012 1080 14 70800785408         . .063373916 .13205482
12 2013 1080 14 66283483136         .  .06100389 .13740344
12 2014 1080 14 61726912512         .  .06142768  .1576208
12 2015 1080 14 34376491008         .  .05720979 .11134155
12 2011 1103 15 75196186624         .   .1048784 .12106317
12 2012 1103 15 70800785408         .  .10637137 .13205482
12 2013 1103 15 66283483136         .  .11095482 .13740344
12 2014 1103 15 61726912512         .  .10666358  .1576208
end

[William Lisowski]

perhaps I am not using the right sintax to get the initial values in 2005. Any advice?

Perhaps start by telling us what the syntax was that caused the problem! Without seeing what you did it's hard to tell you what you could have done better.

Please copy from your Stata results window the command and its full output so we can see what the command was and what the results were. Paste this into your response using code delimiters [CODE] before and [/CODE] after so that the output is readable.

[Hugo Rocha]

Hi!

I am sorry for the lack of clarity. The specification I want to try on Stata is picking the initial values of a variable, for instance, logval_worker at the first year it is reported on the data)

When I do my regression

gen delta1= (F.val_perworker- val_perworker)/val_perworker (growth rate of value added per worker) as the y axis over those initial values I get results omitted for collinearity