Announcement

Collapse
No announcement yet.
X
Collapse
  • Page of 1
  • Filter
  • Time
  • Show
Clear All
new posts
  • #1

    how to combine urban and rural data for each district?

    Hi all,

    I'm new to Stata and I'm using census data to write an undergraduate paper on female labour participation in each district. I have two questions about this.

    1) I want to separate a district into urban and rural and analyze it. I would like to know the command for this.

    2) I use the dummy variable for female labour based on their age and my model includes both age and age squared.
    mean, medium and standard deviation values for both will likely be similar and how can I solve this?

    Could you please guide me on this? I really appreciate your help.

    Here is my example data

    Code:
    * Example generated by -dataex-. For more info, type help dataex
clear
input byte province float(commune district) byte(urban_rural p03_relation p04_sex) int p05_age byte(p07_marital p11_pro p11_dis p11_com p16c_grade_completed p18_activity) int p19_occupation float(childage FL FLEDU)
1 2 1 1 3 1 26 1 1 2 1  6 1 755 0 . .
1 2 1 1 1 1 45 2 1 2 1  2 1 631 0 . .
1 2 1 1 2 2 42 2 1 2 1  4 4   . 0 1 0
1 2 1 1 3 2 23 1 1 2 1  7 1 931 0 1 0
1 2 1 1 3 1  9 1 1 2 1  4 5   . 1 . .
1 2 1 1 3 2 20 1 1 2 1  1 1 931 0 1 0
1 2 1 1 3 2 14 1 1 2 1  7 5   . 1 0 .
1 2 1 1 3 2 11 1 1 2 1  3 5   . 1 0 .
1 2 1 1 3 1  8 1 1 2 1  1 5   . 1 . .
1 2 1 1 3 2 21 1 1 2 1 12 5   . 0 1 0
1 2 1 1 3 1 18 1 1 2 1 11 5   . 0 . .
1 2 1 1 3 1 12 1 1 2 1  5 5   . 1 . .
1 2 1 1 1 2 49 2 1 2 1  5 1 611 0 1 0
1 2 1 1 3 1 22 1 1 2 1  8 1 931 0 . .
1 2 1 1 1 1 39 2 1 2 1  6 1 611 0 . .
1 2 1 1 3 1 12 1 1 2 1  4 5   . 1 . .
1 2 1 1 3 2  7 1 1 2 1  1 5   . 1 0 .
1 2 1 1 1 2 30 2 1 2 1  5 1 611 0 1 0
1 2 1 1 2 1 33 2 1 2 1 10 1 611 0 . .
1 2 1 1 1 1 72 3 1 2 1  6 1 111 0 0 .
1 2 1 1 1 2 66 2 1 2 1  3 4   . 0 0 .
1 2 1 1 3 2 29 1 1 2 1  6 1 931 0 1 0
1 2 1 1 3 1 26 1 1 2 1  6 1 931 0 . .
1 2 1 1 3 2 23 1 1 2 1  6 1 931 0 1 0
1 2 1 1 2 2 41 2 1 2 1  5 1 755 0 1 0
1 2 1 1 3 1 17 1 1 2 1  8 1 755 0 . .
1 2 1 1 3 1 15 1 1 2 1  7 1 755 0 . .
1 2 1 1 3 2 11 1 1 2 1  4 5   . 1 0 .
1 2 1 1 1 1 40 2 1 2 1  7 1 611 0 . .
1 2 1 1 2 2 45 2 1 2 1  7 1 611 0 1 0
1 2 1 1 3 1 27 1 1 2 1  7 1 611 0 . .
1 2 1 1 3 2 21 1 1 2 1  5 1 611 0 1 0
1 2 1 1 3 1 18 1 1 2 1  8 1 611 0 . .
1 2 1 1 1 1 75 2 1 2 1  5 1 611 0 0 .
1 2 1 1 2 2 71 2 1 2 1  2 4   . 0 0 .
1 2 1 1 3 1 46 2 1 2 1  7 1 611 0 . .
1 2 1 1 1 2 52 3 1 2 1  3 1 631 0 1 0
1 2 1 1 3 1 21 1 1 2 1  2 1 755 0 . .
1 2 1 1 1 1 54 4 1 2 1  8 1 411 0 . .
1 2 1 1 3 1 29 1 1 2 1 12 1 931 0 . .
1 2 1 1 3 1 24 1 1 2 1 11 1 931 0 . .
1 2 1 1 1 1 28 2 1 2 1  7 1 755 0 . .
1 2 1 1 2 2 29 2 1 2 1  3 1 755 0 1 0
1 2 1 1 3 1  9 1 1 2 1  2 5   . 1 . .
1 2 1 1 1 1 34 2 1 2 1  8 1 611 0 . .
1 2 1 1 2 2 35 2 1 2 1  8 4   . 0 1 0
1 2 1 1 3 1  9 1 1 2 1  2 5   . 1 . .
1 2 1 1 1 2 64 2 1 2 1  3 1 611 0 1 0
1 2 1 1 3 2 31 1 1 2 1 12 1 611 0 1 0
1 2 1 1 3 1 29 1 1 2 1  2 1 611 0 . .
1 2 1 1 1 1 44 2 1 2 1  8 1 611 0 . .
1 2 1 1 2 2 36 2 1 2 1  3 4   . 0 1 0
1 2 1 1 3 2 13 1 1 2 1  6 5   . 1 0 .
1 2 1 1 3 2 11 1 1 2 1  4 5   . 1 0 .
1 2 1 1 3 1  6 1 1 2 1  0 5   . 1 . .
1 2 1 1 1 2 46 2 1 2 1  8 1 611 0 1 0
1 2 1 1 3 1 23 1 1 2 1 10 1 611 0 . .
1 2 1 1 3 1 22 1 1 2 1 10 1 611 0 . .
1 2 1 1 3 1 19 1 1 2 1  8 5   . 0 . .
1 2 1 1 3 1 14 1 1 2 1  4 5   . 1 . .
1 2 1 1 3 1 10 1 1 2 1  3 5   . 1 . .
1 2 1 1 1 1 64 2 1 2 1  3 1 611 0 . .
1 2 1 1 2 2 57 2 1 2 1  3 1 611 0 1 0
1 2 1 1 3 2 28 1 1 2 1  6 1 611 0 1 0
1 2 1 1 3 1 20 1 1 2 1  5 1 611 0 . .
1 2 1 1 3 1 21 1 1 2 1  5 1 611 0 . .
1 2 1 1 1 1 46 2 1 2 1  8 1 611 0 . .
1 2 1 1 2 2 41 2 1 2 1  5 1 611 0 1 0
1 2 1 1 3 2 20 1 1 2 1 12 5   . 0 1 0
1 2 1 1 3 1 17 1 1 2 1  9 1 611 0 . .
1 2 1 1 3 2 14 1 1 2 1  6 5   . 1 0 .
1 2 1 1 1 2 62 2 1 2 1  5 4   . 0 1 0
1 2 1 1 3 1 27 1 1 2 1  8 1 931 0 . .
1 2 1 1 3 2 40 1 1 2 1  5 1 931 0 1 0
1 2 1 1 1 1 42 2 1 2 1  5 1 611 0 . .
1 2 1 1 2 2 42 2 1 2 1  8 4   . 0 1 0
1 2 1 1 3 2 13 1 1 2 1  6 5   . 1 0 .
1 2 1 1 3 2  8 1 1 2 1  1 5   . 1 0 .
1 2 1 1 1 1 44 2 1 2 1  8 1 611 0 . .
1 2 1 1 2 2 43 2 1 2 1  7 4   . 0 1 0
1 2 1 1 3 1 19 1 1 2 1  9 1 611 0 . .
1 2 1 1 3 1 12 1 1 2 1  5 5   . 1 . .
1 2 1 1 3 2  8 1 1 2 1  1 5   . 1 0 .
1 2 1 1 1 1 38 2 1 2 1  7 1 611 0 . .
1 2 1 1 2 2 40 2 1 2 1  7 4   . 0 1 0
1 2 1 1 3 2 17 1 1 2 1  8 1 611 0 1 0
1 2 1 1 3 1 15 1 1 2 1  7 5   . 0 . .
1 2 1 1 3 2  6 1 1 2 1  0 5   . 1 0 .
1 2 1 1 1 1 47 2 1 2 1  5 1 611 0 . .
1 2 1 1 2 2 47 2 1 2 1  7 4   . 0 1 0
1 2 1 1 3 2 17 1 1 2 1 10 5   . 0 1 0
1 2 1 1 3 2 15 1 1 2 1  8 5   . 0 1 0
1 2 1 1 1 1 48 2 1 2 1  8 1 933 0 . .
1 2 1 1 2 2 45 2 1 2 1  4 4   . 0 1 0
1 2 1 1 1 1 50 2 1 2 1  6 1 931 0 . .
1 2 1 1 2 2 43 2 1 2 1  4 1 931 0 1 0
1 2 1 1 1 1 39 2 1 2 1  5 1 711 0 . .
1 2 1 1 2 2 36 2 1 2 1  2 4   . 0 1 0
1 2 1 1 3 1  8 1 1 2 1  1 5   . 1 . .
1 2 1 1 1 1 49 2 1 2 1  4 1 931 0 . .
end
label values urban_rural URBAN_RURAL
label def URBAN_RURAL 1 "Urban", modify
label values p03_relation P03_RELATION
label def P03_RELATION 1 "Head", modify
label def P03_RELATION 2 "Spouse", modify
label def P03_RELATION 3 "Child", modify
label values p04_sex P04_SEX
label def P04_SEX 1 "Male", modify
label def P04_SEX 2 "Female", modify
label values p05_age P05_AGE
label def P05_AGE 6 "6", modify
label def P05_AGE 7 "7", modify
label def P05_AGE 8 "8", modify
label def P05_AGE 9 "9", modify
label def P05_AGE 10 "10", modify
label def P05_AGE 11 "11", modify
label def P05_AGE 12 "12", modify
label def P05_AGE 13 "13", modify
label def P05_AGE 14 "14", modify
label def P05_AGE 15 "15", modify
label def P05_AGE 17 "17", modify
label def P05_AGE 18 "18", modify
label def P05_AGE 19 "19", modify
label def P05_AGE 20 "20", modify
label def P05_AGE 21 "21", modify
label def P05_AGE 22 "22", modify
label def P05_AGE 23 "23", modify
label def P05_AGE 24 "24", modify
label def P05_AGE 26 "26", modify
label def P05_AGE 27 "27", modify
label def P05_AGE 28 "28", modify
label def P05_AGE 29 "29", modify
label def P05_AGE 30 "30", modify
label def P05_AGE 31 "31", modify
label def P05_AGE 33 "33", modify
label def P05_AGE 34 "34", modify
label def P05_AGE 35 "35", modify
label def P05_AGE 36 "36", modify
label def P05_AGE 38 "38", modify
label def P05_AGE 39 "39", modify
label def P05_AGE 40 "40", modify
label def P05_AGE 41 "41", modify
label def P05_AGE 42 "42", modify
label def P05_AGE 43 "43", modify
label def P05_AGE 44 "44", modify
label def P05_AGE 45 "45", modify
label def P05_AGE 46 "46", modify
label def P05_AGE 47 "47", modify
label def P05_AGE 48 "48", modify
label def P05_AGE 49 "49", modify
label def P05_AGE 50 "50", modify
label def P05_AGE 52 "52", modify
label def P05_AGE 54 "54", modify
label def P05_AGE 57 "57", modify
label def P05_AGE 62 "62", modify
label def P05_AGE 64 "64", modify
label def P05_AGE 66 "66", modify
label def P05_AGE 71 "71", modify
label def P05_AGE 72 "72", modify
label def P05_AGE 75 "75", modify
label values p07_marital P07_MARITAL
label def P07_MARITAL 1 "Never Married", modify
label def P07_MARITAL 2 "Married", modify
label def P07_MARITAL 3 "Widowed", modify
label def P07_MARITAL 4 "Divorced", modify
label values p11_pro P11_PRO
label values p11_dis P11_DIS
label values p11_com P11_COM
label values p16c_grade_completed P16C_GRADE_COMPLETED
label def P16C_GRADE_COMPLETED 0 "Preschool / Kindergarten", modify
label def P16C_GRADE_COMPLETED 1 "Class 1", modify
label def P16C_GRADE_COMPLETED 2 "Class 2", modify
label def P16C_GRADE_COMPLETED 3 "Class 3", modify
label def P16C_GRADE_COMPLETED 4 "Class 4", modify
label def P16C_GRADE_COMPLETED 5 "Class 5", modify
label def P16C_GRADE_COMPLETED 6 "Class 6", modify
label def P16C_GRADE_COMPLETED 7 "Class 7", modify
label def P16C_GRADE_COMPLETED 8 "Class 8", modify
label def P16C_GRADE_COMPLETED 9 "Class 9", modify
label def P16C_GRADE_COMPLETED 10 "Class 10", modify
label def P16C_GRADE_COMPLETED 11 "Class 11", modify
label def P16C_GRADE_COMPLETED 12 "Class 12", modify
label values p18_activity P18_ACTIVITY
label def P18_ACTIVITY 1 "Employed", modify
label def P18_ACTIVITY 4 "Home Maker", modify
label def P18_ACTIVITY 5 "Student", modify
label values p19_occupation P19_OCCUPATION
label def P19_OCCUPATION 111 "Legislators and senior officials", modify
label def P19_OCCUPATION 411 "General office clerks", modify
label def P19_OCCUPATION 611 "Market gardeners and crop growers", modify
label def P19_OCCUPATION 631 "Subsistence crop farmers", modify
label def P19_OCCUPATION 711 "Building frame and related trades workers", modify
label def P19_OCCUPATION 755 "Garment and related trades workers", modify
label def P19_OCCUPATION 931 "Mining and construction labourers", modify
label def P19_OCCUPATION 933 "Transport and storage labourers", modify
    Tags: None
  • #2
    On 1) the more general question:

    Your question title says combine; the question text says separate. I assume you mean the latter.

    Your data example shows a variable Urban_Rural but doesn't show more than some observations with value 1 for Urban.

    So, you've scope to

    1. Run separate analyses for urban and rural using qualifiers such as if Urban_Rural == 1

    2. Use Urban_Rural or some equivalent as a predictor, but make sure you use factor variable notation and also use a version that is coded 0 or 1. In particular, if it is coded 1 or 2, then get yourself a new variable.

    3. Use     Urban_Rural as an outcome. At a guess this is unlikely to be what you want but it might be. If this is an answer, it's essential to use a version that is coded 0 or 1.

    I can't follow what you mean with question 2) beyond guessing that medium is a typo for median. I can't tell if you're talking about results you already have, but either way you'd need to show code and results for a model to get good comments from someone in your field.
    Last edited by Nick Cox; 16 Jun 2024, 07:18.
    • 1 like

    Comment

    • #3
      Hello. I am sorry that my words are quite confusing.

      I want to separate the total value of urban and rural from each district.

      Here is the sample format. I cannot find the correct coding for creating a new dataset like this from my existing dataset.

      Click image for larger version Name: 446065627_1158574612145140_7695374951317027749_n.png Views: 1 Size: 14.6 KB ID: 1756354

      Comment

      • #4
        You've got here at least one variable c3_khum that wasn't in the data example in #1.

        Google Translate can only offer me khum (Mizo) = bed (English).

        Otherwise I am guessing that

        Code:
        help collapse
        may help. If not, you may need to provide more details.

        Comment

        • #5
          c3_khum is renamed as district.

          I want the aggregate value of each variable for urban and rural per district since I need to calculate the dependency ratio of urban and rural areas per district based on the total value.

          I am having trouble creating the format of the dataset.
          Last edited by Mya Thein; 16 Jun 2024, 19:07.

          Comment

          • #6
            not sure what you mean by "aggregate" but -collapse- can compute a large number of summaries including sums; see
            Code:
            h collapse

            Comment

            Previous Next
            Working...
            X