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  • Measure corresponding effect of another observation

    Hi everyone,

    I have the following dataset (example):

    Code:
    * Example generated by -dataex-. For more info, type help dataex
    clear
    input double id int jahr str20 id_owner double(cost taxrate)
    12125 2007 "."               13699991 460
    12125 2008 "."               12924623 460
    12125 2009 "."               12053025 460
    12125 2010 "."                9365376 460
    12125 2011 "."               12585905 460
    12125 2012 "."               12063499 460
    12125 2013 "."               13064557 460
    12125 2014 "."               13081227 460
    12125 2015 "."               12547778 460
    12125 2016 "."               10614449 460
    12125 2017 "."               13216303 460
    12125 2018 "."                8372676 460
    12125 2019 "."                7915701 460
    12741 2009 "DE2070000543"     5676020 335
    12868 2014 "."                1653749 470
    12868 2015 "."                1339976 470
    12868 2016 "."                1183080 470
    12868 2017 "."                 626918 470
    12868 2018 "."                 765542 470
    12868 2019 "."                1138726 470
    12868 2020 "."                1377061 470
    13120 2009 "."                      . 460
    13120 2010 "."                      . 460
    13120 2011 "."                      . 460
    13120 2012 "."                      . 460
    13120 2013 "."                      . 460
    13120 2014 "."                      . 460
    13120 2015 "."                      . 460
    13120 2016 "."                      . 460
    13120 2017 "."                      . 460
    13120 2018 "."                      . 460
    13120 2019 "."                      . 460
    13190 2020 "."                      . 460
    13212 2007 "."                6952019 470
    13212 2008 "."                6853689 470
    13212 2009 "."                4517787 470
    13212 2010 "."                1457154 470
    13212 2011 "."                1473105 470
    13212 2012 "."                      . 470
    13212 2013 "."                      . 470
    13212 2014 "."                      . 470
    13212 2015 "."                      . 470
    13212 2016 "."                      . 470
    13216 2019 "."                      . 460
    14047 2007 "."                      . 450
    14047 2008 "."                      . 445
    14047 2009 "."                      . 445
    14047 2010 "."                      . 445
    15517 2007 "52725"             . 410
    15517 2008 "52725"             . 410
    15517 2012 "52725"             . 410
    15517 2013 "52725"             . 410
    15517 2014 "52725"             . 410
    15517 2015 "52725"      10514000 410
    15517 2016 "52725"      10761000 410
    15517 2017 "52725"      11243000 410
    15517 2018 "52725"      12093000 410
    15517 2019 "52725"      12793000 410
    15517 2020 "52725"      13416000 410
    15668 2007 "."              104936000 360
    15668 2008 "."              108869000 360
    15668 2009 "."              111387000 360
    15668 2010 "."              105874000 360
    15668 2011 "."              116412000 360
    15668 2012 "."              102493000 360
    15668 2013 "."              106203000 360
    15668 2014 "."              105964000 360
    15668 2015 "."              113457000 360
    15668 2016 "."              121847000 360
    15668 2017 "."              123245000 360
    15668 2018 "."              128039000 360
    15668 2019 "."              135717000 360
    15668 2020 "."              147971000 360
    16571 2007 "17881"      43794107 460
    16571 2008 "17881"      37244199 460
    16571 2009 "17881"             . 460
    16571 2010 "17881"      42769669 460
    16571 2011 "17881"      39747546 460
    16571 2012 "17881"      34564477 460
    16571 2013 "17881"      34801441 460
    16571 2014 "17881"      35567573 460
    16571 2015 "17881"      36653536 460
    16571 2016 "17881"      38817938 460
    16571 2017 "17881"      43059493 460
    16571 2018 "17881"      47361387 460
    16571 2019 "17881"      49864124 460
    16571 2020 "17881"      53051354 460
    16677 2019 "13216"      47696000 460
    16677 2020 "13216"      44422294 460
    16697 2007 "110160524765"  26931900 490
    16697 2008 "110160524765"  24779800 490
    16697 2009 "110160524765"  30694400 490
    16697 2010 "110160524765"   3167400 490
    16697 2011 "110160524765"   5439300 490
    16697 2012 "110160524765"   2916300 490
    16697 2013 "110160524765"   3287100 490
    16697 2016 "110160524765"   2120900 490
    16697 2017 "110160524765"   2114700 490
    16697 2018 "110160524765"   2104900 490
    16697 2019 "110160524765"   2062000 490
    end
    format %ty jahr
    Basically I have companys and their direct owner in my dataset. First I want to analyse what with the company costs happens if the tax rate raises or shrinks, which works good with the following code:

    Code:
    xtreg costs log_taxrate i.jahr, fe
    If the tax raise gets a raise, the costs should shrink and the other way around.

    My Problem:

    In a second step I want to analyse what with the costs in the owners company happens. So for example: The tax rate in city A of company A gets a raise of 20% and because of that they cut costs by 5%. Theoretically the cost cut needs to be compensated and the costs in the owners company needs to be raised by 5%.

    For that I need to analyse the effect of the corresponding company and it should be the exact oppoosite, but only if their was a tax change in one of the companys.

    Thank you for your help and best regards

    Nick


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