Cleaning Panel Data for analysis in Latent Gold

[Ciel Pls]

Dear everyone,

I have been stuck on this particular issue for a while now, and I have decided to post here to see whether anyone might be able to help me out.

I am currently using data from 15 (yearly) waves of a panel, however, there was some inflow, so some participants have been added to the study later.
Currently, my data has been set as panel data using an id variable and a variable for 'wave' was created. The data is currently in long format.
The panel holds variables related to employment status, type of contract etc. and education.

I want to follow each participant who entered the labour market for 5 consecutive years (although it is worth mentioning that there is a lot of missingness in the dataset) and adjust for some variables at baseline (the year before they enter the labour market, timepoint 0). Since it is panel data with inflow and participants are aged 16 to 75+, the wave that someone enters the labour market may be different for each individual. I want to have 6 timepoints for each individual: timepoint 0 in which they have not entered the labour market and timepoint 1 being the year they enter the labour market. I want to include individuals who have information of their employment status etc. for at least 3 out of the 5 timepoints available (so from timepoint 1-5). I am not sure how I can go about this, however.

I just want to clean the data as I will be using Latent Gold for a LCA.
I am aware of the fact that I could use R, however, I have no experience with R yet, so I figured STATA might be faster.

Please do let me know your thoughts,
Best wishes,
Ciel

[Clyde Schechter]

How can you tell in the data which year the participant entered the labor market? What do you want to do if there are people who were always in the labor market from the very first wave of the survey onward. If somebody is in the labor market and then drops out, and later returns, does the re-entry count as an entry into the labor market?

Finally, it will be much easier to give you a helpful response is you provide example data using the -dataex- command. If you are running version 18, 17, 16 or a fully updated version 15.1 or 14.2, -dataex- is already part of your official Stata installation. If not, run -ssc install dataex- to get it. Either way, run -help dataex- to read the simple instructions for using it. -dataex- will save you time; it is easier and quicker than typing out tables. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data.

When asking for help with code, always show example data. When showing example data, always use -dataex-.

[Ciel Pls]

Dear Clyde,

Thank you for the quick reply.
To answer your questions: People who have paid work after (at least one year of) education and who are aged between 25-30 would be considered as people who enter the labour market for the first time.
People who were always in the labour market will be excluded, I am specifically interested in first-time entry. If someone enters the labour market twice or more (so they enter and then drop-out to go back to school, for example), the first time they entered the will be considered "entering the labour market", meaning that we will still follow them.

There is a lot of missing data as can be seen from the example below, however, right now, it is hard to determine how much missingness there is and how I want to handle the missing data, as I am not sure how many participants could be included. Although it is evident it would be a small sample.

Below is some example data:
Does this example data help?

Best wishes,
Ciel


800001 1 . . . . .
800001 2 . . . . .
800001 3 . . . . .
800001 4 . . . . .
800001 5 . . . . .
800001 6 . . . . .
800001 7 . . . . .
800001 8 . . . . .
800001 9 . . . . .
800001 10 . . . . .
800001 11 . . . . .
800001 12 . . . . .
800001 13 . . . . .
800001 14 . . . . .
800001 15 . . . . .
800002 1 . . . . .
800002 2 . . . . .
800002 3 . . . . .
800002 4 . . . . .
800002 5 . . . . .
800002 6 . . . . .
800002 7 . . . . .
800002 8 . . . . .
800002 9 . . . . .
800002 10 . . . . .
800002 11 . . . . .
800002 12 . . . . .
800002 13 . . . . .
800002 14 . . . . .
800002 15 1 2 3 3 1
800009 1 . . . . .
800009 2 . . . . .
800009 3 . . . . .
800009 4 . . . . .
800009 5 . . . . .
800009 6 . . . . .
800009 7 0 . . . .
800009 8 0 . . . .
800009 9 0 . . . .
800009 10 0 . . . .
800009 11 . . . . .
800009 12 0 . . . .
800009 13 0 . . . .
800009 14 0 . . . .
800009 15 0 . . . .
800012 1 . . . . .
800012 2 . . . . .
800012 3 . . . . .
800012 4 . . . . .
800012 5 . . . . .
800012 6 . . . . .
800012 7 1 2 3 3 .
800012 8 1 2 4 3 .
800012 9 . . . . .
800012 10 . . . . .
800012 11 . . . . 1
800012 12 . . . . 1
800012 13 . . . . .
800012 14 . . . . .
800012 15 . . . . .
800015 1 . . . . 1
800015 2 . . . . 1
800015 3 1 2 1 2 1
800015 4 1 2 3 2 .
800015 5 1 2 3 2 1
800015 6 1 2 3 2 .
800015 7 1 2 3 2 1
800015 8 1 2 2 3 1
800015 9 1 3 2 2 1
800015 10 0 2 2 3 1
800015 11 1 2 3 2 1
800015 12 1 1 2 2 1
800015 13 1 2 2 2 1
800015 14 1 2 3 2 1
800015 15 1 2 3 2 1
800018 1 . . . . .
800018 2 . . . . .
800018 3 . . . . .
800018 4 . . . . .
800018 5 . . . . .
800018 6 . . . . .
800018 7 . . . . .
800018 8 . . . . .
800018 9 . . . . .
800018 10 . . . . .
800018 11 . . . . .
800018 12 . . . . .
800018 13 . . . . .
800018 14 . . . . .
800018 15 . . . . .
800021 1 . . . . .
800021 2 . . . . .
800021 3 . . . . .
800021 4 . . . . .
800021 5 . . . . .
800021 6 . . . . .
800021 7 0 3 4 3 .
800021 8 . . . . .
800021 9 . . . . 3
800021 10 . . . . .

[Clyde Schechter]

I can probably help you with these clarifications, but I need a usable data example. What you have shown is no the output of -dataex-. Nor is it usable as there is no indication what these variables are. The first, I suppose, is a personal ID and the second, wave number. But there is no clue as to what the subsequent variables are. Please post back with an actual example of your data, and use the -dataex- command so that all of the necesssary metadata will be there. Please re-read my response in #2 about the -dataex- command.

[Ciel Pls]

My apologies!

* Example generated by -dataex-. For more info, type help dataex
clear
input double nomem_encr byte wave double(paidwork educadd paidw pwpast unpaidw lookafloss oblnj ftjobseek interr student carehh primeans flo pension disa volunw curempy monthcj overhours irhours skill support apprec promo uncert pleave) float mh
800001  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  3 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  4 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  5 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  6 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  7 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  8 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001  9 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001 10 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001 11 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001 12 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001 13 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001 14 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800001 15 . . . . . . . . . . . . . . . .    .  . . . . . . . . . 72
800002  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  3 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  4 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  5 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  6 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  7 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  8 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002  9 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002 10 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002 11 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002 12 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002 13 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002 14 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800002 15 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2017  2 2 3 3 3 3 2 1 . 76
800009  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800009  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800009  3 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800009  4 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800009  5 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800009  6 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800009  7 0 . 0 1 0 0 0 0 0 0 1 0 0 0 1 1    .  . . . . . . . . .  .
800009  8 0 . 0 0 0 0 0 0 0 0 0 0 0 0 1 1    .  . . . . . . . . . 80
800009  9 0 . 0 1 0 0 0 0 0 0 0 0 0 0 1 0    .  . . . . . . . . . 80
800009 10 0 . 0 0 0 0 0 0 0 0 1 0 0 0 1 1    .  . . . . . . . . . 88
800009 11 0 . . . . . . . . . . . . . . .    .  . . . . . . . . . 88
800009 12 0 . 0 0 0 0 0 0 0 0 0 1 0 0 0 1    .  . . . . . . . . . 92
800009 13 0 . 0 0 0 0 0 0 0 0 1 0 1 0 1 1    .  . . . . . . . . . 88
800009 14 0 . 0 0 0 0 0 0 0 0 1 0 0 0 1 1    .  . . . . . . . . . 88
800009 15 0 . 0 0 0 0 0 0 0 0 0 0 0 1 0 0    .  . . . . . . . . . 88
800012  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012  3 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012  4 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012  5 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012  6 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012  7 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2002 10 2 3 3 3 3 2 2 .  .
800012  8 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2002 10 2 3 4 4 3 2 1 . 68
800012  9 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012 10 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012 11 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012 12 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012 13 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012 14 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800012 15 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800015  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800015  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800015  3 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 1 3 2 4 1 . 96
800015  4 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 3 3 2 3 2 . 76
800015  5 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 3 3 1 4 2 . 88
800015  6 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 3 2 1 4 3 . 56
800015  7 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 3 2 1 4 3 . 92
800015  8 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 3 2 3 2 1 3 . 88
800015  9 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 3 2 2 3 2 4 2 . 92
800015 10 1 . 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 3 2 3 2 3 1 . 92
800015 11 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 3 3 3 3 1 . 84
800015 12 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 1 2 2 3 3 3 1 . 92
800015 13 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 2 3 3 3 1 . 88
800015 14 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1992  4 2 2 3 3 3 4 2 . 84
800015 15 1 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1994  4 2 2 3 2 2 4 1 . 84
800018  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  3 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  4 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  5 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  6 . . . . . . . . . . . . . . . .    .  . . . . . . . . . 68
800018  7 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  8 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018  9 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018 10 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018 11 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018 12 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018 13 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018 14 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800018 15 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  1 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  2 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  3 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  4 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  5 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  6 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  7 1 . 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2012  8 3 3 4 3 3 . 2 2  .
800021  8 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021  9 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
800021 10 . . . . . . . . . . . . . . . .    .  . . . . . . . . .  .
end
label values paidwork cw22o001
label def cw22o001 0 "No", modify
label def cw22o001 1 "Yes", modify
label values educadd cw22o006
label values paidw cw22o088
label def cw22o088 0 "No", modify
label def cw22o088 1 "Yes", modify
label values pwpast cw22o089
label def cw22o089 0 "No", modify
label def cw22o089 1 "Yes", modify
label values unpaidw cw22o090
label def cw22o090 0 "No", modify
label values lookafloss cw22o091
label def cw22o091 0 "No", modify
label values oblnj cw22o092
label def cw22o092 0 "No", modify
label values ftjobseek cw22o093
label def cw22o093 0 "No", modify
label values interr cw22o094
label def cw22o094 0 "No", modify
label values student cw22o095
label def cw22o095 0 "No", modify
label values carehh cw22o096
label def cw22o096 0 "No", modify
label def cw22o096 1 "Yes", modify
label values primeans cw22o097
label def cw22o097 0 "No", modify
label def cw22o097 1 "Yes", modify
label values flo cw22o098
label def cw22o098 0 "No", modify
label def cw22o098 1 "Yes", modify
label values pension cw22o099
label def cw22o099 0 "No", modify
label def cw22o099 1 "Yes", modify
label values disa cw22o100
label def cw22o100 0 "No", modify
label def cw22o100 1 "Yes", modify
label values volunw cw22o101
label def cw22o101 0 "No", modify
label def cw22o101 1 "Yes", modify
label values monthcj cw22o135
label def cw22o135 2 "February", modify
label def cw22o135 4 "April", modify
label def cw22o135 8 "August", modify
label def cw22o135 10 "October", modify
label values overhours cw22o424
label def cw22o424 1 "often", modify
label def cw22o424 2 "sometimes", modify
label def cw22o424 3 "never", modify
label values irhours cw22o425
label def cw22o425 2 "sometimes", modify
label def cw22o425 3 "never", modify
label values skill cw22o430
label def cw22o430 1 "disagree entirely", modify
label def cw22o430 2 "disagree", modify
label def cw22o430 3 "agree", modify
label def cw22o430 4 "agree entirely", modify
label values support cw22o431
label def cw22o431 2 "disagree", modify
label def cw22o431 3 "agree", modify
label def cw22o431 4 "agree entirely", modify
label values apprec cw22o432
label def cw22o432 1 "disagree entirely", modify
label def cw22o432 2 "disagree", modify
label def cw22o432 3 "agree", modify
label values promo cw22o434
label def cw22o434 1 "disagree entirely", modify
label def cw22o434 2 "disagree", modify
label def cw22o434 3 "agree", modify
label def cw22o434 4 "agree entirely", modify
label values uncert cw22o435
label def cw22o435 1 "disagree entirely", modify
label def cw22o435 2 "disagree", modify
label def cw22o435 3 "agree", modify
label values pleave cw22o440
label def cw22o440 2 "No", modify

This might be correct!

[Clyde Schechter]

Well, we're on the right track now, but not quite there. I assume that we can tell when somebody is doing paid work because the paidwork variable will be 1. But it is unclear how to identify a year of education. There is a variable called student, and perhaps student = 1 defines a year of education? Or must it be both student = 1 and paidwork = 0 at the same time? I don' see anybody with student = 1 and paidwork = 1, but it seems possible in principle. But what then is the variable educadd? The data gives no clue since it is always missing. Anyway, I need an explanation of how to use which variables to ascertain who is doing paid work and who is still in education.

Finally, I don't see anything that looks helpful for identify age. There is no variable whose name suggests either age or date of birth.

[Ciel Pls]

Dear Clyde,

I have thought about your response for a while, hence my late reply. I have made some adjustments to the data.
If someone performs paid work, paidw will be paidw==1. I have added a variable workingstudent, workingstudent==1 for these observations. There is indeed a variable student in which student==1 for people who are in education.
There are a bunch of variables to signify why someone may not be working primeans==1 means someone is living off of private means, flo==1 if someone has taken early retirement or if someone has taken job-related early retirement.
The main issue is thus identifying that someone has entered the labour market while keeping baseline (the year before they enter the labour market) and following them for 5 consecutive years. The variable year-of-birth holds someone's year of birth. Below is a limited data example.

* Example generated by -dataex-. For more info, type help dataex
clear
input double nomem_encr byte wave double(yearofbirth paidw) float workingstudent double student
800001  1 1961 . 0 .
800001  2 1961 . 0 .
800001  3 1961 . 0 .
800001  4 1961 . 0 .
800001  5 1961 . 0 .
800001  6 1961 . 0 .
800001  7 1961 . 0 .
800001  8 1961 . 0 .
800001  9 1961 . 0 .
800001 10 1961 . 0 .
800001 11 1961 . 0 .
800001 12 1961 . 0 .
800001 13 1961 . 0 .
800001 14 1961 . 0 .
800001 15 1961 . 0 .
800002  1 1976 . 0 .
800002  2 1976 . 0 .
800002  3 1976 . 0 .
800002  4 1976 . 0 .
800002  5 1976 . 0 .
800002  6 1976 . 0 .
800002  7 1976 . 0 .
800002  8 1976 . 0 .
800002  9 1976 . 0 .
800002 10 1976 . 0 .
800002 11 1976 . 0 .
800002 12 1976 . 0 .
800002 13 1976 . 0 .
800002 14 1976 . 0 .
800002 15 1976 1 0 0
800009  1 1955 . 0 .
800009  2 1955 . 0 .
800009  3 1955 . 0 .
800009  4 1955 . 0 .
800009  5 1955 . 0 .
800009  6 1955 . 0 .
800009  7 1955 0 0 0
800009  8 1955 0 0 0
800009  9 1955 0 0 0
800009 10 1955 0 0 0
800009 11 1955 . 0 .
800009 12 1955 0 0 0
800009 13 1955 0 0 0
800009 14 1955 0 0 0
800009 15 1955 0 0 0
800012  1    . . 0 .
800012  2    . . 0 .
800012  3    . . 0 .
800012  4    . . 0 .
800012  5    . . 0 .
800012  6    . . 0 .
800012  7    . 1 0 0
800012  8    . 1 0 0
800012  9    . . 0 .
800012 10    . . 0 .
800012 11    . . 0 .
800012 12    . . 0 .
800012 13    . . 0 .
800012 14    . . 0 .
800012 15    . . 0 .
800015  1 1962 . 0 .
800015  2 1962 . 0 .
800015  3 1962 1 0 0
800015  4 1962 1 0 0
800015  5 1962 1 0 0
800015  6 1962 1 0 0
800015  7 1962 1 0 0
800015  8 1962 1 0 0
800015  9 1962 1 0 0
800015 10 1962 0 0 0
800015 11 1962 1 0 0
800015 12 1962 1 0 0
800015 13 1962 1 0 0
800015 14 1962 1 0 0
800015 15 1962 1 0 0
800018  1    . . 0 .
800018  2    . . 0 .
800018  3    . . 0 .
800018  4    . . 0 .
800018  5    . . 0 .
800018  6    . . 0 .
800018  7    . . 0 .
800018  8    . . 0 .
800018  9    . . 0 .
800018 10    . . 0 .
800018 11    . . 0 .
800018 12    . . 0 .
800018 13    . . 0 .
800018 14    . . 0 .
800018 15    . . 0 .
800021  1    . . 0 .
800021  2    . . 0 .
800021  3    . . 0 .
800021  4    . . 0 .
800021  5    . . 0 .
800021  6    . . 0 .
800021  7    . 0 0 0
800021  8    . . 0 .
800021  9    . . 0 .
800021 10    . . 0 .
800021 11    . . 0 .
800021 12    . . 0 .
800021 13    . . 0 .
800021 14    . . 0 .
800021 15    . . 0 .
800028  1 1950 . 0 .
800028  2 1950 . 0 .
800028  3 1950 . 0 .
800028  4 1950 . 0 .
800028  5 1950 . 0 .
800028  6 1950 . 0 .
800028  7 1950 . 0 .
800028  8 1950 . 0 .
800028  9 1950 . 0 .
800028 10 1950 . 0 .
800028 11 1950 . 0 .
800028 12 1950 . 0 .
800028 13 1950 . 0 .
800028 14 1950 . 0 .
800028 15 1950 0 0 0
800033  1    . 1 0 0
800033  2    . 0 0 0
800033  3    . 1 1 1
800033  4    . 1 1 1
800033  5    . 1 1 1
800033  6    . . 0 .
800033  7    . . 0 .
800033  8    . 1 1 1
800033  9    . . 0 .
800033 10    . . 0 .
800033 11    . . 0 .
800033 12    . . 0 .
800033 13    . . 0 .
800033 14    . . 0 .
800033 15    . . 0 .
800039  1    . . 0 .
800039  2    . . 0 .
800039  3    . . 0 .
800039  4    . . 0 .
800039  5    . . 0 .
800039  6    . . 0 .
800039  7    . . 0 .
800039  8    . . 0 .
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800039 10    . . 0 .
800039 11    . . 0 .
800039 12    . . 0 .
800039 13    . . 0 .
800039 14    . . 0 .
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800042  1    . 1 0 0
800042  2    . 1 0 0
800042  3    . 1 0 0
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800042 10    . 1 0 0
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800042 12    . 1 0 0
800042 13    . . 0 .
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800045  1    . . 0 .
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800045  3    . . 0 .
800045  4    . . 0 .
800045  5    . . 0 .
800045  6    . . 0 .
800045  7    . . 0 .
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800045 10    . . 0 .
800045 11    . . 0 .
800045 12    . . 0 .
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800045 15    . . 0 .
800054  1    . . 0 .
800054  2    . . 0 .
800054  3    . . 0 .
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800054  5    . . 0 .
800054  6    . . 0 .
800054  7    . 0 0 0
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800054 11    . 0 0 0
800054 12    . 0 0 0
800054 13    . . 0 .
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800054 15    . . 0 .
800057  1 1975 1 0 0
800057  2 1975 1 0 0
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800057  5 1975 1 0 0
800057  6 1975 . 0 .
800057  7 1975 1 0 0
800057  8 1975 1 0 0
800057  9 1975 1 0 0
800057 10 1975 1 0 0
800057 11 1975 1 0 0
800057 12 1975 1 0 0
800057 13 1975 1 0 0
800057 14 1975 . 0 .
800057 15 1975 1 0 0
800058  1    . . 0 .
800058  2    . . 0 .
800058  3    . . 0 .
800058  4    . . 0 .
800058  5    . . 0 .
800058  6    . . 0 .
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800058 10    . . 0 .
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800058 12    . . 0 .
800058 13    . 1 0 0
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800073  1    . . 0 .
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800073  5    . . 0 .
800073  6    . . 0 .
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800073 10    . . 0 .
800073 11    . 0 0 0
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800073 13    . . 0 .
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800076  1    . . 0 .
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800076  3    . . 0 .
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800076  6    . . 0 .
800076  7    . . 0 .
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800076 11    . . 0 .
800076 12    . . 0 .
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800085  1 1977 . 0 .
800085  2 1977 . 0 .
800085  3 1977 . 0 .
800085  4 1977 . 0 .
800085  5 1977 . 0 .
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800085  7 1977 1 0 0
800085  8 1977 1 0 0
800085  9 1977 . 0 .
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800085 11 1977 1 0 0
800085 12 1977 . 0 .
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800091  1 1983 . 0 .
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800091  3 1983 . 0 .
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800091  6 1983 . 0 .
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800091 10 1983 . 0 .
800091 11 1983 . 0 .
800091 12 1983 . 0 .
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800100  1 1990 . 0 .
800100  2 1990 . 0 .
800100  3 1990 . 0 .
800100  4 1990 . 0 .
800100  5 1990 . 0 .
800100  6 1990 . 0 .
800100  7 1990 0 0 1
800100  8 1990 1 1 1
800100  9 1990 1 0 0
800100 10 1990 0 0 0
800100 11 1990 0 0 1
800100 12 1990 0 0 0
800100 13 1990 1 0 0
800100 14 1990 1 0 0
800100 15 1990 1 1 1
800106  1 2008 . 0 .
800106  2 2008 . 0 .
800106  3 2008 . 0 .
800106  4 2008 . 0 .
800106  5 2008 . 0 .
800106  6 2008 . 0 .
800106  7 2008 . 0 .
800106  8 2008 . 0 .
800106  9 2008 . 0 .
800106 10 2008 . 0 .
800106 11 2008 . 0 .
800106 12 2008 . 0 .
800106 13 2008 . 0 .
800106 14 2008 . 0 .
800106 15 2008 . 0 .
800109  1    . . 0 .
800109  2    . . 0 .
800109  3    . . 0 .
800109  4    . . 0 .
800109  5    . 1 0 0
800109  6    . . 0 .
800109  7    . . 0 .
800109  8    . . 0 .
800109  9    . . 0 .
800109 10    . . 0 .
800109 11    . . 0 .
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800115 15    . . 0 .
800119  1 1950 0 0 0
800119  2 1950 0 0 0
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800119  6 1950 0 0 0
800119  7 1950 0 0 0
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800119 11 1950 0 0 0
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800125  1    . 0 0 0
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800127  1 1983 . 0 .
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800127 14 1983 1 0 0
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800128  1    . . 0 .
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800134  1    . . 0 .
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800155  1    . 1 0 0
800155  2    . . 0 .
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800155  5    . . 0 .
800155  6    . . 0 .
800155  7    . . 0 .
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800155 10    . . 0 .
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800156  1 1974 . 0 .
800156  2 1974 . 0 .
800156  3 1974 . 0 .
800156  4 1974 . 0 .
800156  5 1974 . 0 .
end
label values paidw cw22o088
label def cw22o088 0 "No", modify
label def cw22o088 1 "Yes", modify
label values student cw22o095
label def cw22o095 0 "No", modify
label def cw22o095 1 "Yes", modify

[Clyde Schechter]

OK. I still can't properly implement the age criterion because, while I appreciate your providing the year of birth, there is no indication in your posts or the data what the years of the survey were. Just to illustrate the approach, I have assumed that the survey's first wave took place in 1970--you will see where that asumption shows up in the code, and you can change that to the actual year to get correct age assessments.

I note that in your data, there are no examples that meet all of your criterion for inclusion in your study. In fact, there are none who both were students at some point prior to entering the labor market and then have five consecutive years of paid work.

Anyway, here is some code that implements your criteria as I understand them:

//    VERIFY THAT ALL WAVES ARE PRESENT (EVEN IF ALL DATA IS MISSING IN THE WAVE)
//    FOR ALL ID'S
by nomem_encr (wave), sort: assert wave == _n

// CRITERION 1: ENTRY INTO LABOR MARKET
by nomem_encr (wave), sort: gen prior_paid_work = sum(paidw == 1)
by nomem_encr (wave): egen criterion_1 = ///
    max(prior_paid_work == 1 & prior_paid_work[_n-1] == 0)
//    AND MARK THE (POSSIBLE) BASELINE YEAR
by nomem_encr (wave): egen int baseline_wave = ///
    min(cond(prior_paid_work == 0 & prior_paid_work[_n+1] == 1, wave, .))
    

//    CRITERION 2: EDUCATION PRECEDING ENTRY INTO LABOR MARKET
by nomem_encr (wave): egen criterion_2 = max(student == 1 & prior_paid_work == 0)

//    CRITERION 3: FIVE OR MORE CONSECUTIVE YEARS OF PAID WORK
by nomem_encr: gen byte criterion_3 = prior_paid_work[baseline_wave+5] == 5

//    CRITERION 4: BETWEEN AGES 25 AND 30 IN BASELINE YEAR
local year_first_wave = 1970 // REPLACE THIS BY THE ACTUAL YEAR OF THE FIRST WAVE
gen int age = `year_first_wave' + wave - yearofbirth - 1
by nomem_encr: egen criterion_4 = max(wave == baseline_wave & inrange(age, 25, 30))

//    KEEP ONLY THOSE MEETING ALL FOUR CRITERIAm AND ONLY THE DATA FROM BASELINE
//    THROUGH 5 YEARS OF CONSECUTIVE EMPLOYMENT
keep if criterion_1 & criterion_2 & criterion_3 & criterion_4 ///
    & inrange(wave, baseline_wave, baseline_wave + 5)

[Ciel Pls]

Thank you so much for your time, effort and patience! This helped me out a lot!