Methods for bunch of zeros

Mukesh Punia

Join Date: May 2020
Posts: 60

Methods for bunch of zeros

13 Jan 2024, 13:48

Dear forum members,

I am using individual data from one of the Indian large-scale household surveys with information on 9 digital skills of 877344 individuals aged 15 years and above. The question is asked in yes (1) no (0) form.

I intend to decompose inequality in digital skill by social group (caste in India [ST, SC, OBC, Other])

In the full sample, I have 70% zeros.

I have two questions related to methodology-

1. Shall I go with linear regression and decomposition by taking a total of all nine skills?

Code:

egen digitalskill=rowtotal(b5q8 b5q9 b5q10 b5q11 b5q12 b5q13 b5q14 b5q15 b5q16)
la var digitalskill "total scor of skills"

or
2. Create a binary dependent variable and apply non-linear decomposition

Code:

recode digitalskill (0=0 "no") (1/9=1 "yes"), gen(digtskill)

or
3. Is there any method(s) which can help considering the level of skill

Code:

* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(b5q8 b5q9 b5q10 b5q11 b5q12 b5q13 b5q14 b5q15 b5q16 ST SC OBC Others)
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 1 1 1 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 1 0 0 0 0 0 1 0
1 1 1 0 0 1 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
1 1 1 0 1 1 0 1 0 0 1 0 0
1 1 1 0 1 1 0 1 0 0 1 0 0
1 1 1 0 1 1 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 1 0 1 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 1 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 1 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
1 1 0 0 0 1 0 1 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 1 1 1 1 1 1 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 1 1 1 1 1 1 1 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
end
label values b5q8 b5q8
label def b5q8 0 "no", modify
label def b5q8 1 "yes", modify
label values b5q9 b5q9
label def b5q9 0 "no", modify
label def b5q9 1 "yes", modify
label values b5q10 b5q10
label def b5q10 0 "no", modify
label def b5q10 1 "yes", modify
label values b5q11 b5q11
label def b5q11 0 "no", modify
label def b5q11 1 "yes", modify
label values b5q12 b5q12
label def b5q12 0 "no", modify
label def b5q12 1 "yes", modify
label values b5q13 b5q13
label def b5q13 0 "no", modify
label def b5q13 1 "yes", modify
label values b5q14 b5q14
label def b5q14 0 "no", modify
label def b5q14 1 "yes", modify
label values b5q15 b5q15
label def b5q15 0 "no", modify
label def b5q15 1 "yes", modify
label values b5q16 b5q16
label def b5q16 0 "no", modify
label def b5q16 1 "yes", modify

Last edited by Mukesh Punia; 13 Jan 2024, 13:56.

Best regards,
Mukesh

(Stata 15.1 SE)

Tags: None

Carlo Lazzaro

Join Date: Apr 2014

Posts: 17613
#2

14 Jan 2024, 05:49

Mukesh:
I would support your solution #1.
However, I do not know the specification of the right-hand side of your -regress- equation.

Kind regards,
Carlo
(StataNow 18.5)
Comment

Nick Cox

Join Date: Mar 2014
Posts: 35222

14 Jan 2024, 07:15

Not trying to answer the question -- it's the researcher's prerogative to identify the problem -- but noting that you have 2^9 = 512 composite possibilities and in such a large sample, they may well all occur! But you can still explore their frequencies.

Code:

. egen all = concat(b5q*)

. 
. tab all, sort 

        all |      Freq.     Percent        Cum.
------------+-----------------------------------
  000000000 |         43       43.00       43.00
  110000000 |         14       14.00       57.00
  110001000 |         13       13.00       70.00
  111000000 |          8        8.00       78.00
  100000000 |          7        7.00       85.00
  111001000 |          3        3.00       88.00
  111011010 |          3        3.00       91.00
  111010000 |          2        2.00       93.00
  111011000 |          2        2.00       95.00
  110001010 |          1        1.00       96.00
  111000010 |          1        1.00       97.00
  111011110 |          1        1.00       98.00
  111111110 |          1        1.00       99.00
  111111111 |          1        1.00      100.00
------------+-----------------------------------
      Total |        100      100.00

Comment

Daniel Feenberg

Join Date: Oct 2014

Posts: 321
#4

14 Jan 2024, 08:27

Why not estimate a separate regression for each skill? There are lots of observations.

This might be a job for Zellner's "seemingly unrelated regression" - where the error terms are correlated but the RHS variables are not endogenous.

See: https://www.stata.com/manuals/rsureg.pdf

Linear regression with a binary dependent variable isn't ideal - but a generalization of SUR to Probit is beyond the scope of this reply - or my knowledge.
Comment
Mukesh Punia

Join Date: May 2020

Posts: 60
#5

14 Jan 2024, 10:57

Thank you Dr. Lazzaro

Originally posted by Carlo Lazzaro View Post

Mukesh:
I would support your solution #1.
However, I do not know the specification of the right-hand side of your -regress- equation.

Will zeros not be an issue in ols?

RHS of the specification includes socioeconomic status. Except for age, age^2, and Usual Monthly per capita consumption exp. (MPCE) all others are dummy or categoricals. Given below

Code:

regress digitalskill age i.sex i.edu age_hh i.sex_hh i.edu_hh i.residence i.size_hh mpce i.caste i.religon i.nssregn[iw=wt] if age <=58

Last edited by Mukesh Punia; 14 Jan 2024, 11:00.

Best regards,
Mukesh

(Stata 15.1 SE)
Comment
Mukesh Punia

Join Date: May 2020

Posts: 60
#6

14 Jan 2024, 11:08

Thank you, Dr. Cox

My aim is to decompose social groups (castes in India) based inequality in digital skills in India.

From your suggestion, I am looking for a method that can take into consideration the inequality at 0 & 1 and then at the degrees of skill.

Originally posted by Nick Cox View Post

Not trying to answer the question -- it's the researcher's prerogative to identify the problem -- but noting that you have 2^9 = 512 composite possibilities and in such a large sample, they may well all occur! But you can still explore their frequencies.

Code:

. egen all = concat(b5q*) . . tab all, sort all | Freq. Percent Cum. ------------+----------------------------------- 000000000 | 43 43.00 43.00 110000000 | 14 14.00 57.00 110001000 | 13 13.00 70.00 111000000 | 8 8.00 78.00 100000000 | 7 7.00 85.00 111001000 | 3 3.00 88.00 111011010 | 3 3.00 91.00 111010000 | 2 2.00 93.00 111011000 | 2 2.00 95.00 110001010 | 1 1.00 96.00 111000010 | 1 1.00 97.00 111011110 | 1 1.00 98.00 111111110 | 1 1.00 99.00 111111111 | 1 1.00 100.00 ------------+----------------------------------- Total | 100 100.00

Best regards,
Mukesh

(Stata 15.1 SE)
Comment
Carlo Lazzaro

Join Date: Apr 2014

Posts: 17613
#7

14 Jan 2024, 12:52

Mukesh:
just one note about the RHS of your regression equation: it is recommended to rely on the wonderful capabilities of -fvvarlist- notation (and its relationship with -margins- and -marginsplot-) when it comes to categorical variables and interactions:

Code:

c.age#c.age

Kind regards,
Carlo
(StataNow 18.5)
1 like
Comment

Mukesh Punia

Join Date: May 2020
Posts: 60

15 Jan 2024, 23:44

Respected Jeff Wooldridge kindly share your thoughts on this.

Originally posted by Mukesh Punia View Post

Code:

egen digitalskill=rowtotal(b5q8 b5q9 b5q10 b5q11 b5q12 b5q13 b5q14 b5q15 b5q16)
la var digitalskill "total scor of skills"

or
2. Create a binary dependent variable and apply non-linear decomposition

Code:

recode digitalskill (0=0 "no") (1/9=1 "yes"), gen(digtskill)

or
3. Is there any method(s) which can help considering the level of skill

Code:

* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(b5q8 b5q9 b5q10 b5q11 b5q12 b5q13 b5q14 b5q15 b5q16 ST SC OBC Others)
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 1 1 1 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 1 0 0 0 0 0 1 0
1 1 1 0 0 1 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
1 1 1 0 1 1 0 1 0 0 1 0 0
1 1 1 0 1 1 0 1 0 0 1 0 0
1 1 1 0 1 1 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 1 0 1 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 0 0 0 0 0 0 1 0
1 1 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 1 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
1 1 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 1 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 1 0
1 1 1 0 0 1 0 0 0 0 0 1 0
1 1 1 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
1 1 0 0 0 1 0 1 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 1 1 1 1 1 1 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 1 1 1 1 1 1 1 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
1 1 0 0 0 1 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0
end
label values b5q8 b5q8
label def b5q8 0 "no", modify
label def b5q8 1 "yes", modify
label values b5q9 b5q9
label def b5q9 0 "no", modify
label def b5q9 1 "yes", modify
label values b5q10 b5q10
label def b5q10 0 "no", modify
label def b5q10 1 "yes", modify
label values b5q11 b5q11
label def b5q11 0 "no", modify
label def b5q11 1 "yes", modify
label values b5q12 b5q12
label def b5q12 0 "no", modify
label def b5q12 1 "yes", modify
label values b5q13 b5q13
label def b5q13 0 "no", modify
label def b5q13 1 "yes", modify
label values b5q14 b5q14
label def b5q14 0 "no", modify
label def b5q14 1 "yes", modify
label values b5q15 b5q15
label def b5q15 0 "no", modify
label def b5q15 1 "yes", modify
label values b5q16 b5q16
label def b5q16 0 "no", modify
label def b5q16 1 "yes", modify

Best regards,
Mukesh

(Stata 15.1 SE)

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