Hello,

I am writing my master thesis and I have to find the causal relationship between the Belgian gender quotum in board of directors of listed firms and firm performance. I use the DD method and more specifically, I use xtdidregress in Stata 17. Here you see a little bit of data. The variables are Year, CompanyID (just a number per company), Age, LnSales, ROA, BoardMembers, PercentageIndependentDirectors, CriteriaMet (the year the company met the criteria of the law.


+-----------------------------------------------------------------------------+
| Year Compan~D Age LnSales ROA BoardM~s Percen~s Criter~t |
|-----------------------------------------------------------------------------|
1. | 2008 100 43 8.7498276 14.03 4 0.25 2019 |
2. | 2009 100 44 8.8176829 13.59 4 0.25 2019 |
3. | 2010 100 45 8.8928999 13.31 4 0.25 2019 |
4. | 2011 100 46 8.967963 12.08 4 0.25 2019 |
5. | 2012 100 47 9.0254074 11.16 5 0.40 2019 |
6. | 2013 100 48 9.0655458 11.21 5 0.40 2019 |
7. | 2014 100 49 9.0956924 9.81 5 0.40 2019 |
8. | 2015 100 50 9.1245101 8.98 5 0.40 2019 |
9. | 2016 100 51 9.1583626 9.54 5 0.40 2019 |
10. | 2017 100 52 9.1083741 9.46 5 0.40 2019 |
11. | 2018 100 53 9.1520649 9.22 5 0.40 2019 |
12. | 2019 100 54 9.1675372 9.39 5 0.40 2019 |
13. | 2020 100 55 9.2033862 9.95 6 0.50 2019 |
|-----------------------------------------------------------------------------|
14. | 2008 101 19 9.1235439 4.8 10 0.70 2017 |
15. | 2009 101 20 8.8446859 3.13 10 0.60 2017 |
16. | 2010 101 21 9.1789641 8.53 10 0.60 2017 |
17. | 2011 101 22 9.5805885 9.73 10 0.50 2017 |
18. | 2012 101 23 9.4373177 6.68 10 0.50 2017 |
19. | 2013 101 24 9.1921005 5.27 10 0.40 2017 |
20. | 2014 101 25 9.0857418 4.9 9 0.33 2017 |
21. | 2015 101 26 9.1796425 4.58 9 0.56 2017 |
22. | 2016 101 27 9.253739 3.48 11 0.55 2017 |
23. | 2017 101 28 9.3882576 5.09 10 0.60 2017 |
24. | 2018 101 29 9.526372 6.35 10 0.60 2017 |
25. | 2019 101 30 9.7691032 5.05 10 0.60 2017 |
26. | 2020 101 31 9.9383776 2.32 9 0.67 2017 |
|-----------------------------------------------------------------------------|
27. | 2008 102 78 8.6845703 12.13 13 0.54 2008 |
28. | 2009 102 79 8.6864295 13.6 14 0.57 2008 |
29. | 2010 102 80 8.7875256 17.45 14 0.50 2008 |
30. | 2011 102 81 8.7579409 10.16 14 0.50 2008 |
31. | 2012 102 82 8.7663943 8.73 14 0.50 2008 |
32. | 2013 102 83 8.7385752 8.72 12 0.58 2008 |
33. | 2014 102 84 8.6929935 8.78 13 0.46 2008 |
34. | 2015 102 85 8.6901376 6.91 13 0.54 2008 |
35. | 2016 102 86 8.6706007 7.24 13 0.54 2008 |
36. | 2017 102 87 8.6550403 6.83 11 0.64 2008 |
37. | 2018 102 88 8.659387 6.28 14 0.50 2008 |
38. | 2019 102 89 8.6372847 4.62 14 0.50 2008 |
39. | 2020 102 90 8.6020857 6.78 13 0.54 2008 |
|-----------------------------------------------------------------------------|
40. | 2008 103 203 8.7236869 3.29 11 0.18 2020 |
41. | 2009 103 204 8.7434838 4.85 12 0.00 2020 |
42. | 2010 103 205 8.8612934 5.82 13 0.23 2020 |
43. | 2011 103 206 8.6957242 8.11 13 0.23 2020 |
44. | 2012 103 207 8.6151363 6.63 13 0.23 2020 |
45. | 2013 103 208 8.6071253 4.28 13 0.15 2020 |
46. | 2014 103 209 8.6200385 1.02 13 0.23 2020 |
47. | 2015 103 210 8.7053974 4.94 10 0.30 2020 |
48. | 2016 103 211 8.7751941 2.21 8 0.38 2020 |
49. | 2017 103 212 8.1476067 2.86 8 0.38 2020 |
50. | 2018 103 213 8.1825872 2.62 8 0.50 2020 |
51. | 2019 103 214 8.2424405 2.81 11 0.36 2020 |
52. | 2020 103 215 8.1071175 4.26 11 0.36 2020 |
|-----------------------------------------------------------------------------|
53. | 2008 104 13 7.3348064 24 12 0.25 2018 |
54. | 2009 104 14 7.3570318 22.45 12 0.25 2018 |
55. | 2010 104 15 7.4173521 21.7 12 0.33 2018 |
56. | 2011 104 16 7.413114 17.01 12 0.33 2018 |
57. | 2012 104 17 7.4088184 14.08 12 0.33 2018 |
58. | 2013 104 18 7.2870817 6.59 12 0.33 2018 |
59. | 2014 104 19 7.1302522 3.42 12 0.33 2018 |
60. | 2015 104 20 7.1015368 5.51 12 0.33 2018 |
61. | 2016 104 21 7.1023849 5.4 13 0.31 2018 |
62. | 2017 104 22 7.1125955 3.02 12 0.33 2018 |
63. | 2018 104 23 7.1087015 2.47 12 0.33 2018 |
64. | 2019 104 24 7.162661 2.41 12 0.33 2018 |
65. | 2020 104 25 7.1580135 3.29 12 0.33 2018 |
|-----------------------------------------------------------------------------|
66. | 2008 105 4 10.065054 3.75 13 0.31 2019 |
67. | 2009 105 5 10.512111 6.62 13 0.31 2019 |
68. | 2010 105 6 10.49949 5.86 12 0.33 2019 |
69. | 2011 105 7 10.572496 6.84 12 0.33 2019 |
70. | 2012 105 8 10.590566 7.96 11 0.27 2019 |
71. | 2013 105 9 10.67348 12.5 10 0.30 2019 |
72. | 2014 105 10 10.759242 7.48 11 0.27 2019 |
73. | 2015 105 11 10.682904 7.33 13 0.23 2019 |
74. | 2016 105 12 10.725841 1.88 15 0.20 2019 |
75. | 2017 105 13 10.941004 4.54 15 0.20 2019 |
76. | 2018 105 14 10.908137 2.96 15 0.20 2019 |
77. | 2019 105 15 10.865306 5.39 15 0.20 2019 |
78. | 2020 105 16 10.755368 .75 15 0.20 2019 |
|-----------------------------------------------------------------------------|
79. | 2008 106 13 6.7526313 17.93 10 0.30 2019 |
80. | 2009 106 14 6.1340161 1.49 10 0.30 2019 |
81. | 2010 106 15 6.2635411 3.91 9 0.33 2019 |
82. | 2011 106 16 5.9775101 -.95 10 0.30 2019 |
83. | 2012 106 17 6.0178655 -2.44 9 0.44 2019 |
84. | 2013 106 18 5.9937145 -1.4 11 0.27 2019 |
85. | 2014 106 19 6.1611757 .47 11 0.27 2019 |
86. | 2015 106 20 6.7411185 13.14 13 0.31 2019 |
87. | 2016 106 21 6.5283453 7.74 11 0.55 2019 |
88. | 2017 106 22 6.2409929 1.34 8 0.62 2019 |
89. | 2018 106 23 6.3969697 -1.2 8 0.62 2019 |
90. | 2019 106 24 6.8377372 4.92 9 0.67 2019 |
91. | 2020 106 25 7.115379 13.95 6 0.83 2019 |
|-----------------------------------------------------------------------------|
92. | 2008 107 74 6.5865688 3.64 11 0.45 2021 |
93. | 2009 107 75 6.4584417 -8.87 10 0.50 2021 |
94. | 2010 107 76 6.7990547 7.35 9 0.56 2021 |
95. | 2011 107 77 6.9481714 10.78 9 0.56 2021 |
96. | 2012 107 78 7.0527072 11.86 9 0.44 2021 |
97. | 2013 107 79 7.0544626 6.53 8 0.62 2021 |
98. | 2014 107 80 6.8116496 2.74 8 0.62 2021 |
99. | 2015 107 81 6.9362028 2.08 9 0.67 2021 |
100. | 2016 107 82 7.0051923 1.27 8 0.88 2021 |
101. | 2017 107 83 6.9890643 2.59 10 0.60 2021 |
102. | 2018 107 84 6.9358868 7.72 10 0.50 2021 |
103. | 2019 107 85 6.9870931 9.25 7 0.43 2021 |
104. | 2020 107 86 6.6464983 -.23 7 0.43 2021 |
|-----------------------------------------------------------------------------|
105. | 2008 108 79 6.3838495 5.47 24 0.33 2008 |
106. | 2009 108 80 6.5338324 3.92 24 0.46 2008 |
107. | 2010 108 81 6.4887717 8.09 23 0.35 2008 |
108. | 2011 108 82 6.5650635 4.47 17 0.41 2008 |
109. | 2012 108 83 6.4398391 4.1 20 0.40 2008 |
110. | 2013 108 84 6.4298388 3.53 20 0.40 2008 |
111. | 2014 108 85 6.3891495 3.13 20 0.40 2008 |
112. | 2015 108 86 6.3853816 3.18 20 0.40 2008 |
113. | 2016 108 87 6.3220894 2.67 20 0.40 2008 |
114. | 2017 108 88 6.3571904 3.55 19 0.37 2008 |
115. | 2018 108 89 6.4126045 2.67 19 0.37 2008 |
116. | 2019 108 90 6.4153866 3.28 20 0.40 2008 |
117. | 2020 108 91 6.3758371 3.73 15 0.53 2008 |

As you can see, I have all the data for all companies form 2008 until 2020. The year the law was passed is 2011.
Now I will show you the code I gave stata.

. xtset CompanyID Year, yearly

Panel variable: CompanyID (strongly balanced)
Time variable: Year, 2008 to 2020
Delta: 1 year

. generate Time=(Year>2011)

. generate Treated=.
(663 missing values generated)

. replace Treated=0 if CriteriaMet<=2011
(65 real changes made)

. replace Treated=1 if CriteriaMet>2011
(598 real changes made)

. replace Treated=0 if CriteriaMet<=2011 & Year<2011
(0 real changes made)

. generate TimeTreated=Time*Treated

. xtdidregress (ROA Age LnSales BoardMembers PercentageIndependentDirectors)(TimeTreated), group(CompanyID) time(Year)

The outcome:

xtdidregress (ROA Age LnSales BoardMembers PercentageIndependentDirectors)(TimeTreated), group(CompanyID) time(Year)
note: 2020.Year omitted because of collinearity.

Number of groups and treatment time

Time variable: Year
Control: TimeTreated = 0
Treatment: TimeTreated = 1
-----------------------------------
| Control Treatment
-------------+---------------------
Group |
CompanyID | 5 46
-------------+---------------------
Time |
Minimum | 2008 2012
Maximum | 2008 2012
-----------------------------------

Difference-in-differences regression Number of obs = 663
Data type: Longitudinal

(Std. err. adjusted for 51 clusters in CompanyID)
------------------------------------------------------------------------------------------------
| Robust
ROA | Coefficient std. err. t P>|t| [95% conf. interval]
-------------------------------+----------------------------------------------------------------
ATET |
TimeTreated |
(1 vs 0) | -2.682235 2.406841 -1.11 0.270 -7.516517 2.152048
------------------------------------------------------------------------------------------------
Note: ATET estimate adjusted for covariates, panel effects, and time effects.
I don't understand why they would only use 2008-2012. I have a wider range of data and it is all there. Did I do something wrong?

Thanks in advance for your help
Kind regards
Marie De Tollenaere