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
Code:
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
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