Hi everyone,
I had a question regarding tracking changes within a team, namely if someone new joined the team or someone left the team.
However, I have no idea how to start. It might be that I'm overlooking a straightforward solution, so apologies upfront.
Below is an excerpt of my dataset using dataex.
I know when a firm started (DateIncorporation) (and failed if it failed (dummy)), when a person joined and left (appointment and resignation date, if the resignation date is missing the person is still part of the team), a unique company and employee identifier (BvdIdNumber & DMUci) and some diversity variables (gender, age, nationality).
My goal with this variable is to look at the influence of diversity in the team (blau/coefficient of variation per year) on firm failure (0/1) BUT use the average team changes per year (as some firms exist for 10 vs 2 years, so absolute numbers might not be too valuable) as an interaction effect.
In my dataset, there are currently multiple observations per team member (due to other variables), but if necessary I can delete any duplicates on the above-mentioned variables
So I would need a way to calculate the total number of changes that occurred in the firm lifetime and I would then divide it by the firm age.
Thank you in advance for your advice and help!
Best regards,
Laura
I had a question regarding tracking changes within a team, namely if someone new joined the team or someone left the team.
However, I have no idea how to start. It might be that I'm overlooking a straightforward solution, so apologies upfront.
Below is an excerpt of my dataset using dataex.
I know when a firm started (DateIncorporation) (and failed if it failed (dummy)), when a person joined and left (appointment and resignation date, if the resignation date is missing the person is still part of the team), a unique company and employee identifier (BvdIdNumber & DMUci) and some diversity variables (gender, age, nationality).
My goal with this variable is to look at the influence of diversity in the team (blau/coefficient of variation per year) on firm failure (0/1) BUT use the average team changes per year (as some firms exist for 10 vs 2 years, so absolute numbers might not be too valuable) as an interaction effect.
In my dataset, there are currently multiple observations per team member (due to other variables), but if necessary I can delete any duplicates on the above-mentioned variables
So I would need a way to calculate the total number of changes that occurred in the firm lifetime and I would then divide it by the firm age.
Thank you in advance for your advice and help!
Best regards,
Laura
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input str16 BvdIdNumber float(DateIncorporation FirmFailure DateFailure) str10 DMUci float(AppointmentDate ResignationDate) str1 DMGender str10 DMBirthdate str27 Nationality "AT9070350951" 20454 0 . "P039706490" 20578 . "M" "1978" "Austria" "AT9070350951" 20454 0 . "P206368021" 20578 . "M" "1961" "Austria" "AT9070350951" 20454 1 22410 "P039706490" 20578 . "M" "1978" "Austria" "AT9070350951" 20454 0 . "P039706490" 20578 . "M" "1978" "Austria" "AT9070350951" 20454 1 22410 "P206368021" 20578 . "M" "1961" "Austria" "AT9070350951" 20454 1 22410 "P039706490" 20578 . "M" "1978" "Austria" "AT9070422953" 21164 1 21844 "P301627755" 21445 21808 "M" "10/03/1995" "Austria" "AT9070422953" 21164 1 21844 "P301627755" 21208 . "M" "10/03/1995" "Austria" "AT9070422953" 21164 1 21844 "P300272480" 21208 . "M" "17/04/1993" "Austria" "AT9110749953" 18263 1 21122 "P098788430" 19886 . "M" "27/03/1985" "Bulgaria" "AT9110939024" 20461 1 21825 "P001968694" 20488 . "M" "30/03/1962" "Germany" "AT9110939024" 20461 1 21825 "P117216063" 20488 . "M" "1968" "Germany" "BE0501515635" 19318 1 21774 "P238808799" 20121 21774 "M" "20/06/1984" "Belgium" "BE0501515635" 19318 1 21774 "P144431731" 19318 21774 "M" "21/12/1983" "Belgium" "BE0501562254" 19318 0 . "P123993699" 19814 . "M" "17/08/1988" "Belgium" "BE0501562254" 19318 1 19907 "P044740365" 19318 21487 "M" "25/03/1972" "Belgium" "BE0501562254" 19318 1 19907 "P123993699" 19814 . "M" "17/08/1988" "Belgium" "BE0501562254" 19318 0 . "P044740365" 19318 21487 "M" "25/03/1972" "Belgium" "BE0501562254" 19318 1 19907 "P123993699" 19814 . "M" "17/08/1988" "Belgium" "BE0501562254" 19318 1 19907 "P044740365" 19318 21487 "M" "25/03/1972" "Belgium" "BE0505804718" 20054 0 . "P246173452" 20240 20800 "M" "1974" "Japan" "BE0505804718" 20054 0 . "P238328579" 20240 20800 "M" "1968" "Japan" "BE0505804718" 20054 1 20800 "P246173452" 20240 20800 "M" "1974" "Japan" "BE0505804718" 20054 1 20800 "P238328579" 20240 20800 "M" "1968" "Japan" "BE0505804718" 20054 0 . "P103923129" 20054 20800 "M" "1973" "Germany" "BE0505804718" 20054 1 20800 "P103923129" 20054 20800 "M" "1973" "Germany" "BE0518917237" 19414 0 . "P042037775" 22140 . "M" "08/03/1946" "Belgium" "BE0518917237" 19414 0 . "P042037775" 19414 . "M" "08/03/1946" "Belgium" "BE0518917237" 19414 0 . "P044189380" 19414 . "F" "05/03/1946" "Belgium" "BE0518917237" 19414 0 . "P044189380" 19414 . "F" "05/03/1946" "Belgium" end format %td DateIncorporation format %td DateFailure format %td AppointmentDate format %td ResignationDate

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