Dear all,
Sorry for bringing up a topic that has been discussed in this forum several times , but I cannot find any helpful insights after reading the previous posts. I am currently doing the research on analysing the effect of Chief Digital Officer (CDO) on firm financial performance by replicating the methodology proposed by Bertrand and Schoar (2003). So basically I choose the Chief Digital Officers that have been in at minimum of two firms during my sample period in order to separate managerial fixed effect from the firm fixed effect, and they should be at each firm for at least one full year. The dummy variable CDO is defined as 1 if for a firm i at time t it has the CDO j. But what troubles me is that I cannot separate managerial fixed effect from firm fixed effect by using the following command:
or alternatively,
Once the above commands are applied, i.CDO_id will be omitted by Stata due to collinearity except one CDO in my case.
However, if I set the fixed effect the other way around, then it seems to work:
The followings are my sample data:
I would really appreciate any advice on how to solve this problem. Specially, I wonder if it is correct to set the fixed effect in the second that I mentioned above (i.e., y controls i.company, absorb(Year) ).
Sorry for bringing up a topic that has been discussed in this forum several times , but I cannot find any helpful insights after reading the previous posts. I am currently doing the research on analysing the effect of Chief Digital Officer (CDO) on firm financial performance by replicating the methodology proposed by Bertrand and Schoar (2003). So basically I choose the Chief Digital Officers that have been in at minimum of two firms during my sample period in order to separate managerial fixed effect from the firm fixed effect, and they should be at each firm for at least one full year. The dummy variable CDO is defined as 1 if for a firm i at time t it has the CDO j. But what troubles me is that I cannot separate managerial fixed effect from firm fixed effect by using the following command:
Code:
encode Company_id,gen(company) gen lnassets = log(Totalassets) areg ROA CDO* CF Leverage Investment Cashholdings lnassets i.Year, absorb(company) vce(robust) testparm CDO*
Code:
encode Company_id,gen(company) gen lnassets = log(Totalassets) areg ROA CF Leverage Investment Cashholdings lnassets i.CDO_id i.Year, absorb(company) vce(robust)
However, if I set the fixed effect the other way around, then it seems to work:
Code:
encode Company_id,gen(company) gen lnassets = log(Totalassets) areg ROA CDO* CF Leverage Investment Cashholdings lnassets i.company, absorb(Year) vce(robust) testparm CDO*
The followings are my sample data:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input long company int Year long CDO_id byte CDO double(CF Leverage Investment Cashholdings) float lnassets 3 2012 2099971 0 .019565658615790543 .819335705812574 .08556931195453622 .10525674852851634 10.64137 3 2013 2099971 0 .056068811723478815 .8298892119116839 .07916533928002549 .09198789423383243 10.606857 3 2014 2099971 0 .08007633891296569 .8304290874449252 .0801558586139716 .08938014393065882 10.570445 3 2015 2099971 0 .06584324766142947 .8647743376132606 .10480428662246843 .0783761692852602 10.504246 3 2016 2099971 0 .0022532451628704386 .8744156778137361 .11486651971589518 .08689688954200343 10.494713 3 2017 2099971 0 .00039416633819471815 .8901417238613067 .10726251478123769 .0811489948758376 10.40765 3 2018 2099971 0 .10310338381005796 .8574792305300102 .0991306786315199 .08641802206019816 10.389642 3 2019 2099971 1 .05906581368854614 .872597380506889 .10538077293839278 .06261502059416353 10.42371 3 2020 2099971 1 .04822302064845678 .8850082947699293 .0822475217523051 .0616423531448855 10.451695 3 2021 2099971 0 .03205509314308363 .9065814393939394 .10483551327784384 .058214427269124056 10.40314 3 2022 2099971 0 .021671297285744816 .9377142411966345 .1945287454584313 .08993374652703569 10.55485 3 2023 2099971 0 .04538862153075351 .9435356922866334 .2545981936844881 .0600237326125651 10.70994 14 2012 2099971 0 .5160075329566854 .6960208062418726 .30836522689994533 .33351558228540185 11.396054 14 2013 2099971 0 .4026939970717423 .6446269701678592 .2603806734992679 .3561346998535871 11.349182 14 2014 2099971 1 .338722326114496 .7011297317556353 .2038366411292755 .4428424926102431 11.346646 14 2015 2099971 0 .3454319453076445 .7196433304305024 .20267246737103792 .40149160969546305 11.26884 14 2016 2099971 0 .1936431275290432 .7368389423076923 .19109776791541574 .4678240438585041 11.22129 14 2017 2099971 0 .2565171317555634 .7180236747297993 .06344048039561992 .5836100317908867 11.251067 14 2018 2099971 0 .6566229556505083 .7225055344718533 .21482245469279504 .57882716958892 11.270968 14 2019 2099971 0 .6408929627439385 .7241248888972939 .19729450029568302 .6123595505617978 11.270255 14 2020 2099971 0 .41756382651491847 .7113567044470385 .16264226391879422 .7191633343586589 11.26861 14 2021 2099971 0 .6953204876130554 .6998270053719384 .19441604404246954 .7278018088871412 11.3241 14 2022 2099971 0 .7087039390088945 .7030557062929211 .20640088945362134 .5562261753494282 11.31378 14 2023 2099971 0 .9428074947113931 .6635833362095744 .23360531882744032 .5271985494106981 11.37912 5 2012 1950347 0 2.723963599595551 .22739047189998873 .28816986855409504 .8149646107178968 11.751525 5 2013 1950347 0 2.5859375 .2307477626658232 .2021484375 .6591796875 11.724158 5 2014 1950347 0 3.119301648884578 .20047583759116971 .2793404461687682 .6372453928225025 11.594312 5 2015 1950347 0 2.482421875 .21949965729952023 .2958984375 .4833984375 11.558434 5 2016 1950347 0 2.12112676056338 .25271749950228944 .29389671361502345 .40938967136150234 11.59552 5 2017 1950347 0 3.425373134328358 .2343519338647771 .2789179104477612 .5755597014925373 11.630015 5 2018 1950347 0 2.555980861244019 .25034927041291527 .26507177033492824 .47751196172248805 11.628475 5 2019 1950347 0 3.3746928746928746 .23354475438482947 .26597051597051596 .2076167076167076 11.69483 5 2020 1950347 0 4.318620689655172 .22820849759088918 .21241379310344827 .26 11.743934 5 2021 1950347 0 2.142857142857143 .26716252571609433 .2753391859537111 .6089385474860335 11.50731 5 2022 1950347 0 -.361998361998362 .3494741270509045 .343980343980344 .6027846027846028 11.49261 5 2023 1950347 1 .5048923679060665 .34300162995778827 .26125244618395305 .7064579256360078 11.545993 40 2012 1950347 0 .9465064162592594 .2746583504475039 .11087502664023094 1.5405157548711257 8.650051 40 2013 1950347 0 .743973215362572 .24436980207378886 .07713608930785448 1.7030533135857742 9.375039 40 2014 1950347 0 .7950652701201807 .4087537292768794 .061704045701535495 1.5831079634029117 9.413333 40 2015 1950347 0 .782465142903872 .4989083905274559 .06520116939935397 1.4208662141718116 9.447694 40 2016 1950347 0 .7839686031991437 .5162744995717069 .05518225605042517 1.7742111607842714 9.499447 40 2017 1950347 0 .8035117849435984 .48894071267313727 .07370956679807515 1.8756823669140565 9.532275 40 2018 1950347 0 1.0551637674345713 .1077623881166026 .08196207490988873 1.9611290214252133 9.494501 40 2019 1950347 0 .8592905015544885 .20780431306551264 .07841229471473891 1.6242527367596946 9.510287 40 2020 1950347 0 .894177520994409 .24000384119286497 .1002624578668526 8.671119420549873 9.714252 40 2021 1950347 1 .804080800176485 .24035817629529388 .11388279562283815 9.435410223644832 9.775406 40 2022 1950347 0 .44947439537371414 .5360709969915317 .10706731117241601 2.97100126273797 9.798986 40 2023 1950347 0 .7434821319718191 .40172780501010896 .14807945943349873 3.6040586820551286 9.837464 6 2012 2264218 0 .25860323886639675 .863571278163824 .5381723539618276 .5431607865818392 12.113667 6 2013 2264218 0 .18001674574379012 .8576014423446896 .5133128663131454 .30873569634384596 11.92614 6 2014 2264218 0 .2070433240435774 .8385130043614276 .5008360780339498 .2825437040790474 11.929205 6 2015 2264218 0 .24778868290318673 .8536599370679161 .2838361807827457 .3865261117169514 11.97402 6 2016 2264218 0 .29115313185885533 .8337826705609226 .27638021273003544 .5009285834880972 12.00596 6 2017 2162824 0 .3031423290203327 .8048110164465595 .44057844949440034 .46232467108839836 12.026635 6 2018 2162824 0 .32424006235385816 .8032854940102004 .4129829640351854 .5051775971495379 12.09441 6 2019 2162824 1 .337879727216367 .7338748384714787 .41568505889646623 .36732796032238063 12.104283 6 2020 2162824 1 .2503087299325544 .6233090797294527 .4103733257338273 1.4838985465944714 12.112668 6 2021 2264218 1 .35435460062325735 .5388036329470717 .41987862883385274 .4151221912415942 12.11239 6 2022 2264218 1 .25703659876595386 .6583206304933026 .298537739836024 .4708815822838306 12.164344 6 2023 2264218 1 .21502418666413733 .6470914810930849 .26169022099971545 .6587309115052642 12.187868 29 2012 2162824 0 .884808189541449 .2629084720935743 .7488259194765448 2.3309060207904597 5.836272 29 2013 2162824 0 1.1791420354689248 .1916200248756219 .3085956223038824 1.4876178303243328 6.06461 29 2014 2162824 0 .34601320899118454 .34307363179096045 .4066441792567816 1.1310411292837097 6.512213 29 2015 2162824 0 .38401991923770606 .12454461660009655 .486042168770849 1.8013024116961678 6.95904 29 2016 2162824 0 .2788852631416155 .11106545933224302 .16840135475789114 3.7929818592350877 7.470373 29 2017 2162824 0 .6195984115442698 .057312129312595335 .6943285418647576 1.6682756306281115 7.922433 29 2018 2162824 0 .25839161371912667 .0029433332592618032 .4248411573798967 1.2344509454373696 8.208069 29 2019 2162824 0 .13615598766029258 .00492132055123276 .13327528941343395 .8192326020516018 8.47904 29 2020 2162824 0 .3133102168806674 .7312205856111069 .08095515433896332 1.2528397366421489 8.971106 29 2021 2162824 0 .7841970723908808 .3096225544427904 .012831187863245016 .8680410997685224 9.013521 29 2022 2162824 1 .7481750161418967 .09563065765382016 .16617038246800103 1.5976147973717194 9.195785 29 2023 2162824 1 .36904231021285017 .028555746879035726 .1816302868377275 2.2599183344255684 9.330026 7 2012 2264218 1 1.5056396148555709 .7673364296009757 .2896836313617607 6.121045392022008 11.939108 7 2013 2264218 1 1.6461935483870969 .7558330306711586 .25961290322580644 5.0286451612903225 11.94064 7 2014 2264218 1 1.7513966480446928 .7482218541433234 .3034535297105129 5.659725749111224 11.977307 7 2015 2264218 1 1.5107108081791627 .7189106137655344 .32643622200584227 5.540895813047712 11.990302 7 2016 2264218 0 1.4595082337017822 .7194410991898401 .31017369727047145 5.686442589668396 11.975986 7 2017 2264218 0 .93993993993994 .7638958923238044 .24532224532224534 7.606144606144606 12.107334 7 2018 2264218 0 1.860054347826087 .7340508035746245 .2966485507246377 6.214900362318841 12.147394 7 2019 2264218 0 1.6439801406702523 .7358726015478316 .3402978899462143 5.0570955730244105 12.197642 7 2020 2264218 0 .9328015952143569 .6610729347922258 .294715852442672 6.573280159521436 12.161948 7 2021 2264218 0 1.9556936647955092 .6485141453363975 .3107457898957498 4.416198877305534 12.147108 7 2022 2264218 0 1.7526366251198466 .6399492947896025 .35570469798657717 6.503163950143816 12.338653 7 2023 2264218 0 1.9511483067341378 .6366426647326979 .3042039704165045 9.068898404048268 12.47269 2 2012 1978032 0 1.5195031814833078 .16474936519404518 .26389440709809187 7.787252903465391 8.634149 2 2013 1978032 0 1.5425968817583058 .1554293377706365 .24219012891329886 9.215228732060666 8.761198 2 2014 1978032 0 1.2391753504856835 .15500580213494414 .28583983213960945 4.605344926753874 8.833418 2 2015 1978032 0 1.4269581282700161 .14638238541693657 .2390274953035972 4.702501125584139 8.862027 2 2016 1978032 0 1.684368888693257 .2511203942709228 .20027322060756217 6.375870124316949 8.983475 2 2017 1978032 0 1.1842438932759205 .4358656462267796 .1843197532023726 .9462965906381625 9.958983 2 2018 1978032 0 1.997736345302202 .3597937076550918 .2208003271167294 .707416782751523 9.925169 2 2019 1978032 0 1.7821980362118017 .31927706745850715 .22571678924973915 .5314162668679799 9.970802 2 2020 1978032 0 1.8130954048909431 .31328328447842546 .1478652210812263 .9422601714676845 9.974347 2 2021 1978032 0 1.245574772959363 .15779451805028843 .173656969931548 .9994507468478578 10.865173 2 2022 1978032 1 2.101383248435017 .15987916559103815 .2912201037436326 .6124055929611577 10.825807 2 2023 1978032 1 1.6038550762689494 .17173030802257896 .3607914707138275 .2740153593276698 10.795372 39 2012 1978032 0 .5356364768129473 .317668207498716 .16588857765328355 .49455337690631807 9.868171 39 2013 1978032 0 .5947567909033481 .2645251886958048 .19425142135186355 .44314592545799114 9.823416 39 2014 1978032 0 .7986301369863014 .29988340458608626 .21746575342465754 .8414383561643836 9.911058 39 2015 1978032 0 1.0397260273972602 .28836587719949514 .2054794520547945 1.140068493150685 9.932512 end label values company company label def company 2 "ADI", modify label def company 3 "AES", modify label def company 5 "ALL", modify label def company 6 "ALLY", modify label def company 7 "APX", modify label def company 14 "CAT", modify label def company 29 "LOB", modify label def company 39 "TEL", modify label def company 40 "TRMK", modify
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