Hello readers,
I am trying to decompose (using Ben Jann’s nldecompose) differences in the probability of having more liberal gender views into differences in characteristics (cohort replacement) and differences in coefficients (intra-cohort change).

The commands and output are as follows:

Code:
. nldecompose, by(Period): logit ideology1 Age [pweight=weight]

                                                   Number of obs (A) =    1872
                                                   Number of obs (B) =    1403

------------------------------------------------------------------------------
      Results |      Coef.  Percentage
--------------+---------------------------------------------------------------
 Omega = 1    |
         Char |  -.0078933   7.860032%
         Coef |  -.0925304   92.13997%
--------------+---------------------------------------------------------------
 Omega = 0    |
         Char |  -.0064641   6.436787%
         Coef |  -.0939597   93.56321%
--------------+---------------------------------------------------------------
          Raw |  -.1004237        100%
------------------------------------------------------------------------------

.
I have read the stata manual but I have a few questions if anyone could kindly clarify:

1.What is the difference or justification for using twofold over threefold? I understand the difference between them but not when to use each one (i.e. in twofold you are saying that if the observed variables have the same effect in each period, then it would explain x% of observed disparity in gender views – in threefold you add disparity in returns of these observed variables)

2. In a number of academic papers I have read which use nldecompose, the authors have decomposed change into:
  • Differences attributable to observable characteristics (“Char”)
  • Differences not attributable to observable characteristics (“Coef)
  • Total difference
I can see the first two parts in my output (char and coef) but how does one get the “total difference”?

For example in:

Arndt, B.J., 2017. Explaining Income-Related Disparities in Pap Smear Utilization: A regression-based decomposition analysis of differences in Pap smear utilization following implementation of the Affordable Care Act (No. 1694-2017-5829). – TABLE A3

Kelly, E., McGuinness, S., O’connell, P.J., Haugh, D. and Pandiella, A.G., 2014. Transitions in and out of unemployment among young people in the Irish recession. Comparative Economic Studies, 56(4), pp.616-634. – TABLE 7

An example of my data:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(Period ideology1) int Age
0 1 43
0 1 48
1 1 34
1 1 24
0 0 27
1 1 24
0 0 32
1 1 36
1 0 38
1 1 38
0 1 71
0 1 39
0 0 60
0 1 25
0 1 56
0 1 51
0 0 34
0 1 19
0 1 70
0 1 62
0 1 36
1 0 45
1 1 27
1 1 16
0 1 37
1 1 38
0 1 30
1 1 52
0 1 38
1 1 17
0 1 58
0 1 42
0 1 16
0 1 41
0 1 47
0 1 41
0 1 16
0 1 40
0 1 33
0 0 51
0 1 44
0 1 53
0 0 36
0 0 58
0 1 65
0 0 52
0 1 17
0 1 20
0 1 44
0 0 38
0 1 47
0 1 65
0 1 41
0 1 41
0 1 19
0 0 56
0 1 58
0 1 50
0 1 65
0 1 51
0 1 32
0 1 18
0 1 38
0 0 51
0 0 56
0 0 77
0 0 31
0 1 40
0 1 49
0 0 53
0 1 40
0 0 63
0 0 43
0 1 51
0 1 52
0 1 49
0 1 60
0 1 36
0 1 35
0 1 76
0 1 36
0 1 46
0 1 69
0 1 55
0 0 35
0 1 25
0 1 46
0 1 60
0 0 38
0 0 50
0 1 16
0 1 62
0 1 18
0 0 35
0 1 31
0 0 73
0 1 68
0 1 50
0 1 60
0 1 33
end
label values Period per
label def per 0 "1999-2004", modify
label def per 1 "2005-2009", modify
label values ideology1 edc
label def edc 0 "Agree", modify
label def edc 1 "Disagree", modify
label values Age X003