HELP needed please in "Difference in Difference research design"

[Maysam Ayoub]

Hello everyone,

I am working on my thesis and would like to check if my Diff in Diff code is correct:


Dependent Variable: I have one dummy and one continuous.
Independant Variable: dummy variable
Controls: CONTROL1 and CONTROL2
Fixed Effect: Country, Industry and Year
Clustering: at firm level (ID)

I used the below code for my entire sample, and I am getting non-significant results and result contradicting prior research.
I would appreciate it if you could tell me if this code is wrong:

* Generate panel variable
egen panel = group(COUNTRY INDUSTRY YearEnded)

* Set panel data structure
xtset panel

* Fixed effects regression with reghdfe
reghdfe DEPVAR_DUMMY i.INDVAR_DUMMY CONTROL1 CONTROL2, absorb(YearEnded INDUSTRY COUNTRY) vce(cluster ID)
reghdfe DEPVAR_CONT i.INDVAR_DUMMY CONTROL1 CONTROL2, absorb(YearEnded INDUSTRY COUNTRY) vce(cluster ID)

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Code:
* Example generated by -dataex-. For more info, type help dataex clear input str12 ID str25 COUNTRY int YearEnded float(INDVAR_DUMMY INDUSTRY DEPVAR_DUMMY DEPVAR_CONT CONTROL1 CONTROL2 panel) "AT0000A0E9W5" "AUSTRIA" 2011 1 4 0 .018158501 1.4508354e-06 -.6408805 1 "AT0000A0E9W5" "AUSTRIA" 2012 1 4 0 .010819887 1.6463787e-06 .4787839 2 "AT0000A00XX9" "AUSTRIA" 2013 1 4 0 .05179885 2.9177265e-06 -.015922926 3 "AT0000A0E9W5" "AUSTRIA" 2013 1 4 0 .15087678 3.2949536e-06 -.54731053 3 "AT0000A0E9W5" "AUSTRIA" 2014 1 4 0 .029307505 3.016278e-06 -.13406444 4 "AT0000A0E9W5" "AUSTRIA" 2015 1 4 0 .024541363 2.0785521e-06 .10426443 5 "AT0000A0E9W5" "AUSTRIA" 2016 1 4 0 .5931679 2.8788706e-06 .4271086 6 "AT0000808209" "AUSTRIA" 2016 1 5 0 .04755795 .000011926285 -.08508099 10 "AT0000A0E9W5" "AUSTRIA" 2017 1 4 0 .0381135 1.4186143e-06 .7145636 7 "AT0000946652" "AUSTRIA" 2019 1 4 0 .07355142 9.947606e-07 .03514466 8 "AT0000946652" "AUSTRIA" 2020 1 4 0 .1449236 1.0177748e-06 -.08289425 9 "GRS371113002" "GREECE" 2011 1 4 0 .008094379 4.0178547e-06 -.07783944 11 "CH0198251305" "GREECE" 2012 1 4 1 .0792848 1.0669667e-07 .06715062 12 "GRS470003013" "GREECE" 2014 1 5 1 .02390172 1.8760026e-06 -.0395707 14 "GRS260333000" "GREECE" 2015 1 5 0 .09092028 1.0553861e-07 .0004985356 15 "GRS316003003" "GREECE" 2016 1 5 0 .068146005 2.74039e-06 -.016552616 16 "GRS239003007" "GREECE" 2016 1 6 0 .02118119 3.472284e-06 -.02756531 19 "GRS371113002" "GREECE" 2017 1 4 0 .07979685 6.022467e-06 .1616823 13 "GRS316003003" "GREECE" 2017 1 5 0 .01215596 2.850361e-06 .08140419 17 "GRS239003007" "GREECE" 2017 1 6 1 .0341728 3.277052e-06 .1990406 20 "GRS239003007" "GREECE" 2019 1 6 1 .0488752 2.7273386e-06 -.1038168 21 "GRS495003006" "GREECE" 2021 1 5 0 .13253261 5.6650543e-07 .1369414 18 "AT0000A18XM4" "AUSTRIA" 2011 0 4 0 .1162596 2.56354e-06 .14337115 1 "AT0000764626" "AUSTRIA" 2012 0 4 0 .13983518 2.9506366e-06 .06047976 2 "AT0000762406" "AUSTRIA" 2013 0 4 0 .09486791 2.2260608e-06 -.16968694 3 "AT0000922554" "AUSTRIA" 2013 0 4 1 .13533801 1.7568974e-06 .24266697 3 "AT0000A00XX9" "AUSTRIA" 2014 0 4 1 .023363186 2.655334e-06 -.23707242 4 "AT0000762406" "AUSTRIA" 2015 0 4 0 .02863175 2.2083325e-06 .41082165 5 "AT0000762406" "AUSTRIA" 2016 0 4 1 .06054421 2.295456e-06 -.09278757 6 "AT00000VIE62" "AUSTRIA" 2016 0 5 1 .07729094 4.8099065e-07 .021511994 10 "AT0000785555" "AUSTRIA" 2017 0 4 0 .0987419 9.170525e-07 .14288542 7 "AT0000837307" "AUSTRIA" 2019 0 4 0 .08285154 8.395476e-07 -.1201961 8 "AT0000785555" "AUSTRIA" 2020 0 4 0 .010106214 1.268421e-06 .2317951 9 "GRS300103009" "GREECE" 2011 0 4 0 .1570957 2.975871e-06 .1953435 11 "GRS074083007" "GREECE" 2012 0 4 1 .0589695 2.3579595e-07 .019381227 12 "GRS316003003" "GREECE" 2014 0 5 0 .04671111 1.9960487e-06 -.08188433 14 "GRS496003005" "GREECE" 2015 0 5 0 .05448818 7.223308e-07 .032065466 15 "GRS513003004" "GREECE" 2016 0 5 0 .036939748 2.79546e-06 -.033706553 16 "GRS096003009" "GREECE" 2016 0 6 0 .032032546 2.1950693e-06 -.00739915 19 "GRS074083007" "GREECE" 2017 0 4 0 .07480128 3.400548e-07 .07312657 13 "GRS470003013" "GREECE" 2017 0 5 0 .06763208 2.629779e-06 .08179094 17 "GRS096003009" "GREECE" 2017 0 6 0 .032597233 2.2640754e-06 .1620873 20 "GRS403003007" "GREECE" 2019 0 6 0 .0821615 1.7852096e-06 .02309672 21 "GRS359353000" "GREECE" 2021 0 5 0 .00430444 5.049148e-07 .032421682 18 end
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Listed 44 out of 44 observations

[George Ford]

where are the variables for the treatment group and treatment period?

also, not good to dichotomize a continuous variable unless you've got good reason.

g

[Carlo Lazzaro]

Maysam:
you have already posted this query (with replies) at: Diff in Diff for a matched sample - Statalist.
If those replies are not helpful (and -xtdidregress- does not help out either), I would think of how to make your question more informative. Thanks.

[Maysam Ayoub]

Dear Carlo and Goerge,

I hope you can help me with this. My problem is that I need to know how to convert my dataset in a way that I can apply DID method.
I am giving an example below where I want to assess the impact of Office Change (Dummy Variable) on Accruals (Continuous):

Company ID	OFFICE_CHG	Year	Industry	Accruals	Control Variable	Country
ABC	1	2010	Finance	0.1	100	UK
DEF	1	2011	Finance	0.3	125	UK
GHI	1	2012	Finance	0.6	900	UK
XYZ	0	2010	Finance	0.4	265	UK
PQR	0	2011	Finance	0.5	800	UK
JKL	0	2012	Finance	0.7	523	UK
QWE	1	2010	Manufacturing	0.4	973	US
RTY	1	2011	Manufacturing	0.8	936	US
UIO	1	2012	Manufacturing	0.5	287	US
ASD	0	2010	Manufacturing	0.3	342	US
FGH	0	2011	Manufacturing	0.2	687	US
JKL	0	2012	Manufacturing	0.8	758	US

For each instance of office change in a specific year and industry and country, I have matched it with a non office change situation but I am not knowing how to convert the layout of the dataset.



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* Example generated by -dataex-. For more info, type help dataex
clear
input str3 CompanyID byte OFFICE_CHG int Year str13 Industry double Accruals int ControlVariable str2 Country
"ABC" 1 2010 "Finance"       .1 100 "UK"
"DEF" 1 2011 "Finance"       .3 125 "UK"
"GHI" 1 2012 "Finance"       .6 900 "UK"
"XYZ" 0 2010 "Finance"       .4 265 "UK"
"PQR" 0 2011 "Finance"       .5 800 "UK"
"JKL" 0 2012 "Finance"       .7 523 "UK"
"QWE" 1 2010 "Manufacturing" .4 973 "US"
"RTY" 1 2011 "Manufacturing" .8 936 "US"
"UIO" 1 2012 "Manufacturing" .5 287 "US"
"ASD" 0 2010 "Manufacturing" .3 342 "US"
"FGH" 0 2011 "Manufacturing" .2 687 "US"
"JKL" 0 2012 "Manufacturing" .8 758 "US"
end

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Listed 12 out of 12 observations


Thank you in advance

[George Ford]

For DID, you need a period (periods) where all units are untreated and a period (period) where some units are treated.

This data looks like a a company is either treated or not, always.