I have the following question:

I want to estimate the effect of retirement on the 'cesd-score' (which indicates someones mental health, using a panel dataset and the first difference model:

reg d.cesd d.retired d.age d.female d.education d.mstat2 d.mstat3 d.mstat4 d.white

My question is: should I include the constant or not? What do I base my decision on?

My data:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long id byte(wave education mstat) int age byte cesd float(female white retired)
    3010  1 12 1  56  . 0 1 0
    3010  2 12 1  58  0 0 1 0
    3010  3 12 1  60  3 0 1 0
    3010  4 12 1  62  3 0 1 0
    3010  5 12 1  64  1 0 1 0
    3010  6 12 1  66  1 0 1 0
    3010  7 12 1  68  0 0 1 0
    3010  8 12 1  70  0 0 1 1
    3010  9 12 1  72  0 0 1 1
    3010 10 12 1  74  0 0 1 1
    3010 11 12 1  76  0 0 1 1
10001010  2 12 4  55  4 0 1 1
10001010  3 12 4  57  1 0 1 0
10001010  4 12 4  58  5 0 1 0
10001010  5 12 4  60  1 0 1 1
10001010  6 12 4  62  1 0 1 1
10001010  7 12 4  64  1 0 1 1
10001010  8 12 4  66  1 0 1 1
10001010  9 12 4  69  1 0 1 1
10001010 10 12 4  71  1 0 1 1
10001010 11 12 4  72  1 0 1 1
10001010 12 12 4  74  0 0 1 1
10003020  1 16 1  58  . 0 1 0
10003020  2 16 1  60 .m 0 1 0
10003020  3 16 1  62 .m 0 1 0
10003020  4 16 1  64 .m 0 1 1
10003030  1 16 1  36  . 1 1 0
10003030  2 16 1  38  1 1 1 0
10003030  3 16 1  40  3 1 1 0
10003030  4 16 1  42  3 1 1 0
10003030  6 16 3  46  1 1 1 0
10003030  8 16 3  50  4 1 1 1
10003030 10 16 3  54  0 1 1 1
10003030 11 16 3  56  0 1 1 1
10003030 12 16 2  58  1 1 1 1
10083010  4 10 1  59  2 0 0 0
10083010  5 10 1  61  1 0 0 0
10083010  6 10 1  63  0 0 0 1
10083010  7 10 1  65  0 0 0 1
10083010  8 10 1  67  0 0 0 1
10083010  9 10 1  69  1 0 0 1
10094010  1 12 3  58  . 1 0 0
10114010  1 12 4  55  . 1 0 0
10114010  2 12 4  56  2 1 0 1
10114010  3 12 4  58  4 1 0 1
10114010  4 12 4  60  0 1 0 1
10114010  5 12 4  62  1 1 0 1
10124011  5 12 1 100 .m 0 0 0
10155010  1  7 2  53  . 1 0 0
10155010  2  7 2  55  1 1 0 0
10155010  3  7 2  57  0 1 0 0
10155010  4  7 2  59  1 1 0 0
10225010  1  8 4  57  . 1 0 0
10225010  2  8 4  59  7 1 0 0
10225010  3  8 4  61  8 1 0 1
10225010  4  8 4  63  5 1 0 1
10225010  5  8 4  65  2 1 0 1
10225010  6  8 4  67  1 1 0 1
10225010  7  8 4  69  1 1 0 1
10225010  8  8 4  71  0 1 0 1
10225010  9  8 4  73  1 1 0 1
10225010 10  8 4  76  2 1 0 1
10225010 11  8 4  77  2 1 0 1
10225010 12  8 4  79  4 1 0 1
10240010  1  9 2  53  . 0 1 0
10240010  2  9 2  55  1 0 1 0
10240010  6  9 2  63  8 0 1 1
10325020  3 14 1  57  0 1 1 0
10325020  4 14 1  59  0 1 1 0
10325020  5 14 1  60  1 1 1 0
10325020  6 14 1  63  0 1 1 1
10325020  7 14 1  65 .m 1 1 1
10325020 11 14 3  73  0 1 1 1
10325020 12 14 3  74  0 1 1 1
10346010  1 11 4  52  . 0 1 0
10372010  1 10 4  56  . 1 0 0
10372010  2 10 4  58  4 1 0 0
10372010  3 10 4  60  6 1 0 0
10372010  4 10 4  62  3 1 0 0
10372010  5 10 4  64  6 1 0 1
10372010  6 10 4  66  5 1 0 1
10372010  7 10 4  68  5 1 0 1
10372010  8 10 4  70  6 1 0 1
10372010  9 10 4  72  2 1 0 1
10372010 10 10 4  75  1 1 0 1
10372010 11 10 4  76  4 1 0 1
10372010 12 10 4  78  3 1 0 1
10378010  1 16 4  53  . 1 0 0
10378010  2 16 4  54  0 1 0 0
10378010  4 16 1  58  5 1 0 0
10378010  5 16 4  60  1 1 0 0
10378010  6 16 4  62  1 1 0 1
10378010  7 16 1  64  1 1 0 1
10394010  5 16 1  59  3 0 1 0
10394010  8 16 1  65  0 0 1 0
10404010  1 12 3  52  . 1 1 0
10404010  2 12 2  54  1 1 1 0
10404010  3 12 2  56  3 1 1 0
10404010  4 12 3  58  0 1 1 0
10404010  5 12 3  60  0 1 1 0
end
label values education EDYRS
label values mstat marital
label def marital 1 "Married or in partnership", modify
label def marital 2 "Separated or divorced", modify
label def marital 3 "Widowed", modify
label def marital 4 "Single", modify