ineqdeceo - calculate gini for different subgroups with contribution to total

[gokhan ekonomi]

Hi everyone; i m new on Stata. So I m new on Stata so i had to consult here. i have wealth variables of person who died. these people have som honorific titiles. i want to see intra-group and between group inequality but also want to see gini contributions to overall gini. so i used ineqdeco command, and then "return list" .

. return list

scalars:

r(ede2) = 20330.27098899694
r(ede1) = 44576.47437925962
r(edehalf) = 66521.25365573603
r(between_a2) = .1202628999210008
r(between_a1) = .0617805982848419
r(between_ahalf) = .0287198015642444
r(within_a2) = .7599909809184966
r(within_a1) = .5065551779746393
r(within_ahalf) = .2886993717996624
r(a2_3) = .7825259936571144
r(a1_3) = .5225974917411804
r(ahalf_3) = .2913051749565403
r(a2_2) = .7740103721558161
r(a1_2) = .5377354621887207
r(ahalf_2) = .3282960461482253
r(a2_1) = .683186016995525
r(a1_1) = .4126750826835632
r(ahalf_1) = .2095925837965658
r(between_ge2) = .0464823953855145
r(between_ge1) = .0416233751258453
r(between_ge0) = .0384595274391314
r(between_gem1) = .0365809050768381
r(within_ge2) = 1.121531331904797
r(within_ge1) = .6562591383432529
r(within_ge0) = .7316561329462238
r(within_gem1) = 1.831461826609987
r(sumw_3) = 97
r(v_3) = .4145299145299146
r(lambda_3) = 1.066504602831005
r(theta_3) = .442098061857297
r(lgmean_3) = 11.53946375368956
r(mean_3) = 102689.3564948454
r(gini_3) = .5920576594975053
r(ge2_3) = .9069654585267596
r(ge1_3) = .6274381621047558
r(ge0_3) = .7393952772397693
r(gem1_3) = 1.799125345636311
r(sumw_2) = 111
r(v_2) = .4743589743589743
r(lambda_2) = .7633332675532834
r(theta_2) = .3620939858906601
r(lgmean_2) = 11.20501662166448
r(mean_2) = 73498.23135135135
r(gini_2) = .6200976741671276
r(ge2_2) = 1.956877415016125
r(ge1_2) = .8253289149049929
r(ge0_2) = .7716179184731826
r(gem1_2) = 1.712490921683989
r(sumw_1) = 26
r(v_1) = .1111111111111111
r(lambda_1) = 1.762271570268389
r(theta_1) = .1958079522520432
r(lgmean_1) = 12.04168082072447
r(mean_1) = 169681.9057692308
r(gini_1) = .4910979418316964
r(ge2_1) = .4434495360779261
r(ge1_1) = .4086826175970001
r(ge0_1) = .5321770871783036
r(gem1_1) = 1.078213168681186
r(a2) = .7888551615604363
r(a1) = .537040480861859
r(ahalf) = .3091277846940984
r(ge2) = 1.168013727290313
r(ge1) = .697882513469098
r(ge0) = .7701156603853567
r(gem1) = 1.868042731686836
r(gini) = .6116433251381541
r(p75p50) = 2.884325969362665
r(p25p50) = .4374031565176243
r(p10p50) = .2008837692643143
r(p90p50) = 4.98278853558737
r(p75p25) = 6.594204743116542
r(p90p10) = 24.80433612847651
r(p95) = 336984
r(p90) = 214812
r(p75) = 124345.6
r(p50) = 43110.8
r(p25) = 18856.8
r(p10) = 8660.26
r(p5) = 4571
r(max) = 1201480
r(min) = 1723.2
r(N) = 234
r(sumw) = 234
r(sd) = 147479.4936055905
r(Var) = 21750201034.16142
r(mean) = 96285.90089743589


my overall Gini= 0.61164; so i could not calculate with these results to total gini. (with their contributions) If anyone help me i would be glad.

[Stephen Jenkins]

The Gini coefficient is not additively decomposable by population subgroup in general, unlike generalised entropy inequality indices. See ineqdecgini (by me, SSC), including discussion and references in the help-file. The presence of negative values may cause issues. I recommend using targeted searches using -search- and relevant keywords, or indeed Googling more generally, would be appropriate before posting again. Good luck

[kashif mansoor]

Did not want to start a new topic. so writing here my querry. Expect replies from Professors.
I am using ineqdeco to calculate wage inequality. I want to calculate wage inequality among two different classes of workers ; casual and regular salaried. When I run ineqdeco command, I dont get results.
An example of dataset is below

* Example generated by -dataex-. To install:    ssc    install    dataex
clear
input float dailywage byte cws_worker
5.714286 2
6 2
6.571429 2
7.142857 2
7.142857 2
7.142857 2
7.142857 2
7.714286 2
8.285714 2
8.571428 2
9.285714 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10 2
10.428572 2
10.714286 2
10.714286 2
10.714286 2
10.714286 2
10.714286 2
10.714286 2
10.714286 2
10.714286 2
11.428572 2
11.428572 2
11.428572 2
11.428572 2
11.428572 2
12.142858 2
12.142858 2
12.142858 2
12.714286 2
12.857142 2
12.857142 2
12.857142 2
12.857142 2
12.857142 2
13.285714 2
13.285714 2
13.285714 2
13.285714 2
13.285714 2
13.285714 2
13.285714 2
13.285714 2
13.428572 2
13.571428 2
13.714286 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
end
label values cws_worker workers
label def workers 2 "Regular worker", modify

result turns to be

Kindly help.

[Nick Cox]

In your data example all observations show cws_worker equal to 2.

[kashif mansoor]

This shows in the example dataset. I have nonetheless cws_worker=3 and 1 also. I guess the dataset is sorted. I am resharing another dataex example

* Example generated by -dataex-. To install: ssc install dataex
clear
input byte cws_worker
1
3
1
1
1
1
3
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
3
1
3
1
1
3
1
3
1
1
3
1
1
1
1
1
1
1
1
1
1
1
3
1
1
3
1
1
1
3
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
3
3
1
1
3
1
1
1
3
1
1
1
1
1
2
1
1
1
3
1
1
1
1
1
1
1
3
3
3
end
label values cws_worker workers
label def workers 1 "Self-employed", modify
label def workers 2 "Regular worker", modify
label def workers 3 "Casual worker", modify

[kashif mansoor]

Nick Cox please help.

[Nick Cox]

We need a data example with both variables that shows the problem reported in #3.

[kashif mansoor]

Sorry, Nick. Here is the data example. Hope this suffices

* Example generated by -dataex-. To install: ssc install dataex
clear
input float dailywage byte cws_worker
 4.285714 2
        5 3
 6.857143 2
 6.857143 2
 7.142857 2
 7.142857 2
 8.333333 3
 8.571428 2
 8.571428 3
       10 3
       10 2
       10 3
       10 2
       10 3
       10 2
       10 3
       10 2
       10 3
       10 2
10.142858 2
10.714286 2
10.714286 2
10.714286 3
10.714286 3
11.428572 3
11.428572 2
11.428572 3
12.571428 2
12.857142 2
12.857142 2
12.857142 2
12.857142 2
12.857142 2
       13 2
       13 2
       14 2
14.285714 3
14.285714 3
14.285714 3
14.285714 3
14.285714 2
14.285714 2
14.285714 3
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 2
14.285714 3
14.285714 3
14.285714 3
14.285714 3
14.285714 3
14.285714 3
14.285714 3
14.285714 3
14.285714 2
14.285714 2
14.285714 3
14.285714 2
14.285714 3
14.285714 3
14.285714 2
14.285714 3
14.285714 2
14.285714 3
14.285714 2
       15 3
       15 2
       15 3
       15 3
       15 3
15.714286 2
15.714286 3
15.714286 3
15.714286 2
       16 3
       16 2
       16 3
       16 2
16.142857 2
16.571428 2
16.571428 2
16.571428 2
16.714285 2
16.714285 3
16.714285 3
16.714285 3
16.714285 3
16.714285 2
16.714285 2
16.714285 2
16.714285 2
16.714285 2
16.714285 2
16.714285 2
       17 2
17.142857 3
17.142857 2
17.142857 3
17.142857 2
17.142857 2
17.142857 3
17.142857 2
17.142857 3
17.142857 3
17.142857 2
17.142857 3
17.142857 3
17.142857 2
17.142857 2
17.142857 3
17.142857 3
17.142857 3
17.142857 3
17.142857 2
17.142857 3
17.285715 2
17.857143 2
17.857143 2
17.857143 3
17.857143 3
17.857143 2
17.857143 3
17.857143 2
17.857143 2
17.857143 3
17.857143 2
17.857143 3
17.857143 2
17.857143 2
17.857143 3
17.857143 3
17.857143 2
       18 2
       18 2
       18 2
       18 3
       18 2
18.333334 3
18.333334 3
18.571428 3
18.571428 2
18.571428 2
18.571428 3
18.571428 3
18.571428 2
18.571428 3
       19 2
19.285715 2
19.333334 3
19.714285 2
       20 3
       20 2
       20 2
       20 2
       20 2
       20 3
       20 3
       20 3
       20 3
       20 3
       20 3
       20 3
       20 2
       20 3
       20 3
       20 2
       20 2
       20 2
       20 2
       20 2
       20 3
       20 3
       20 2
       20 3
       20 2
       20 2
       20 2
       20 3
       20 3
       20 3
       20 2
       20 3
       20 3
       20 2
       20 2
       20 2
       20 3
       20 2
       20 3
20.285715 2
20.333334 3
 20.57143 2
20.714285 2
20.714285 3
20.833334 3
 21.42857 2
 21.42857 2
end
label values cws_worker workers
label def workers 2 "Regular worker", modify
label def workers 3 "Casual worker", modify

[Nick Cox]

Thanks for the data example. That works for me.

. ineqdeco dailywage, by(cws_worker)
 
Percentile ratios

----------------------------------------------------------
  All obs |    p90/p10     p90/p50     p10/p50     p75/p25
----------+-----------------------------------------------
          |      1.918       1.167       0.608       1.340
----------------------------------------------------------
  
Generalized Entropy indices GE(a), where a = income difference
 sensitivity parameter, and Gini coefficient

----------------------------------------------------------------------
  All obs |     GE(-1)       GE(0)       GE(1)       GE(2)        Gini
----------+-----------------------------------------------------------
          |    0.03832     0.03100     0.02669     0.02405     0.11969
----------------------------------------------------------------------
   
Atkinson indices, A(e), where e > 0 is the inequality aversion parameter

----------------------------------------------
  All obs |     A(0.5)        A(1)        A(2)
----------+-----------------------------------
          |    0.01424     0.03052     0.07118
----------------------------------------------
  
Subgroup summary statistics, for each subgroup k = 1,...,K:
  

------------------------------------------------------------------------------------------
    cws_worker |   Popn. share           Mean  Relative mean   Income share      log(mean)
---------------+--------------------------------------------------------------------------
Regular worker |       0.54000       16.02778        0.99075        0.53500        2.77432
 Casual worker |       0.46000       16.35326        1.01086        0.46500        2.79443
------------------------------------------------------------------------------------------
  
Subgroup indices: GE_k(a) and Gini_k 

---------------------------------------------------------------------------
    cws_worker |     GE(-1)       GE(0)       GE(1)       GE(2)        Gini
---------------+-----------------------------------------------------------
Regular worker |    0.04350     0.03475     0.02967     0.02660     0.12610
 Casual worker |    0.03200     0.02648     0.02316     0.02107     0.11175
---------------------------------------------------------------------------
  
Within-group inequality, GE_W(a)

----------------------------------------------------------
  All obs |     GE(-1)       GE(0)       GE(1)       GE(2)
----------+-----------------------------------------------
          |    0.03827     0.03095     0.02664     0.02400
----------------------------------------------------------
              
Between-group inequality, GE_B(a):

----------------------------------------------------------
  All obs |     GE(-1)       GE(0)       GE(1)       GE(2)
----------+-----------------------------------------------
          |    0.00005     0.00005     0.00005     0.00005
----------------------------------------------------------
              
Subgroup Atkinson indices, A_k(e)

---------------------------------------------------
    cws_worker |     A(0.5)        A(1)        A(2)
---------------+-----------------------------------
Regular worker |    0.01588     0.03415     0.08003
 Casual worker |    0.01227     0.02613     0.06014
---------------------------------------------------
  
Within-group inequality, A_W(e)

----------------------------------------------
  All obs |     A(0.5)        A(1)        A(2)
----------+-----------------------------------
          |    0.01420     0.03042     0.07079
----------------------------------------------
 
Between-group inequality, A_B(e)

----------------------------------------------
  All obs |     A(0.5)        A(1)        A(2)
----------+-----------------------------------
          |    0.00004     0.00010     0.00043
----------------------------------------------

I am using ineqdeco 2.1.0 from SSC, as below.

. which ineqdeco

*! 2.1.0 SPJ Feb 2021 Add pweight so that use PVK's -rhsbsample- & svy bootstrap 
*! 2.0.2 SPJ May 2008 (fix bug arising if bygroup() and `touse' lead to no obs in a group)
*!   bug fix method provided by Austin Nichols (many thanks!)
*! 2.0.1 SPJ August 2006 (new vbles created as doubles)
*! 2.0.0 SPJ August 2006 (port to Stata 8.2; additional saved results), 
*!   with initial code rewriting contribution from Nick Cox (many thanks!)
*! version 1.6 April 2001 (made compatible with Stata 7; SSC)
*! version 1.0.1 Stephen P. Jenkins, April 1998   STB-48 sg104
*! Inequality indices, with optional decomposition by population subgroups

[kashif mansoor]

The above data example is from a survey round of 2011-12. ineqdeco command produces the desired result. However, the command does not give the desired result when applied to a different survey round of 2009-10. The data example from 2009-10 round is produced below.

copy starting from the next line ------ ----------------

* Example generated by -dataex-. To install: ssc install    dataex
clear
input float dailywage byte cws_worker
60 3
80 3
70 3
100 3
50 3
85.71429 3
80 3
50 3
150 3
80 3
50 3
50 3
100 3
71.42857 3
110 3
60 3
64.28571 3
100 3
70 3
80 3
90 3
30 3
. 3
120 3
80 3
100 3
30 3
77.57143 2
140 3
142.85715 2
235.7143 2
747.5714 2
178.57143 2
110 3
150 3
267.85715 2
100 3
50 3
214.2857 2
66.71429 2
70 3
100 3
285.7143 2
71.42857 2
357.14285 2
200 2
100 3
100 3
111.66666 3
100 3
214.2857 2
150 3
40 2
464.2857 2
700 2
333 2
300 3
50 3
214.2857 2
368.85715 2
178.57143 2
321.4286 2
364.2857 2
110 3
117.85714 2
100 3
80 3
80 3
150 3
225 3
120 3
80 3
150 3
60 3
150 3
120 3
408.2857 2
130 3
214.2857 3
125.71429 3
150 3
106 3
100 3
217.14285 3
120 3
107.14286 3
106 3
100 3
160 3
100 3
85.71429 3
120 3
150 3
433.2857 2
80 3
200 3
300 3
100 3
75 3
100 3
end
label values cws_worker workers
label def workers 2 "Regular worker", modify
label def workers 3 "Casual worker", modify

[Nick Cox]

Once again, it works for me in the sense that I see output. But "does not give the desired result" could mean something else.

[kashif mansoor]

it gives results for percentile ratios, and Atkinson indices but not the GE indices including the Gini coefficient. This problem persists even when I remove dailywage =0 from the dataset. This problem is faced only in 2009-10 round not other rounds.
Nonetheless, thanks a lot.