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Support #502

Weights to use to analyse year-to-year transitions within waves 1-5

Added by Sanne Velthuis about 5 years ago. Updated about 5 years ago.

Status:
Closed
Priority:
Normal
Start date:
02/09/2016
% Done:

100%


Description

Hi,

I have a question about which weights to use. A similar question (no. 462) has been asked before, but I'm still not 100% sure about what to do.

I have created a panel dataset of all working age individuals in waves 1-5. At the moment I've just left all observations in the panel so it is an unbalanced panel. I'm interested in low pay transitions, and what I would specifically like to do is calculate, for individuals who are low-paid in any given year, the transition probabilities to higher pay, to unemployment, self-employment and economic inactivity. In other words, I want to know what proportion of workers who are low-paid in one year are still low-paid the following year, what proportion are higher-paid the following year, what proportion are unemployed, and so forth. The syntax I was planning to use for this would look something like:

tab paystatus if L.paystatus == 1 (1 being low-paid)

I would like to calculate this across all waves. So I am not specifically interested in transition rates from wave 1 to 2, from wave 2 to 3, from wave 3 to 4, etc. I just want to know the 'average' transition rates from low pay to other pay/labour market states within all waves from 1 to 5 (so within the period 2009-2014). But I am not sure what weights to use to do this.

Should I create a balanced panel and use the longitudinal weight for wave 5, applying this to all waves for each respondent as suggested in the User Guide on p. 61? But I am not necessarily interested only in those individuals who were present in the sample from waves 1 to 5. If someone was only present at waves 1, 2, and 3, but was low-paid at any point during this period, then I'd like to be able to include them. Or will it mess up my estimates if I do this?

In question 462 it was suggested pooled analysis could be used if the aim is to represent events, rather than people. However, I don't really understand the difference between these, nor which one of these applies to my situation. I am interested in transitions - which are technically events - but the question I'm trying to answer is what proportion of low-paid people have made the transition to higher pay the following year. So would pooled analysis still be suitable? And if so, what weights should I use? In the above-mentioned question the suggestion was, as far as I understand, to use the wave 2 longitudinal weight for transitions between waves 1 and 2, the wave 3 longitudinal weight for transitions between waves 2 and 3, etc. If I understand this correctly, this means that I could just use the longitudinal weight for each wave as it is provided in the data, without having to copy the weights across all waves for each respondent, right?

A further question is whether or not I should include former BHPS respondents (and therefore use the _indinub, rather than the _indinus weights)? Or will it screw up my estimates to conduct my analysis from wave 1 onwards but include the BHPS sample from wave 2 onwards?

If you could provide some advice about these matters that would be greatly appreciated.

Many thanks in advance,

Sanne

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