Understanding Society User Support

Dear USOC team,

I am working on a project exploring the prevalence of mental health for people living in in-work poverty and have some questions on poverty rates and weighting that I hope you can help with. My analysis only uses UKHLS wave 3 onwards. My questions are below.

Thank you in advance,
Sam

1. For this analysis I am interested in financial years rather than waves. In constructing my financial years I have followed the process set out in the user guide and in this forum (months 4 to 15 from wave n, months 16 to 24 from wave n-1 and months 1-3 from wave n+1). I am aware that I need to adjust my cross-sectional weights to account for attrition in a similar manner as is set out in box 1 of the weighting FAQ. I note that box 1 includes sum ind [aw=b_indpxub_xw] if b_month>=1 & b_month<=12 but this old issue (https://iserredex.essex.ac.uk/support/issues/1221) implies the line should end with 24 instead of 12. Could I clarify which is correct?

2. As I am combining three waves in each financial year, I'm currently adjusting to the average weighted total of the three waves which I have seen suggested elsewhere in this forum. Previously I was adjusting by a factor such as wave_1_total / wave_2_total but switched to this method because of slight increases in sample size in some waves. Can I confirm this is an appropriate method for the adjustment? Please note that I have assumed the answer to question 1 is 24, and therefore I am accounting for the entire wave weight, rather than just the specific months I am combining:

gen count = 1
summarize count [aw = `wave_1'_hh_weight]
local `wave_1'_total = r(sum)
...
local avg = (``wave_1'_total' + ``wave_2'_total' + ``wave_3'_total') / 3
gen weight = weight * (`avg' / ``wave_1'_total') if `wave'_month >= 16 & `wave'_month <= 24
replace weight = weight * (`avg' / ``wave_2'_total') if `wave'_month >= 4 & `wave'_month <= 15
replace weight = weight * (`avg' / ``wave_3'_total') if `wave'_month >= 1 & `wave'_month <= 3

3. For part of my analysis I am comparing poverty rates between this data and the FRS, which I expect to be similar but not identical. This is in part because of methodological differences and also because I do not have access to the Council Tax data in Understanding Society so my results will not be perfect. To generate my poverty rates I have followed worksheet 4 in the into to USOC using STATA. However my poverty rates from USOC are quite a bit below the FRS and also below figure 10 here (https://www.understandingsociety.ac.uk/sites/default/files/downloads/working-papers/2019-08.pdf). Have other users reported a similar issue? I'd be happy to share my code if useful.

Fin_Year FRS US
2012/13 15.4% 12.9%
2013/14 15.2% 13.0%
2014/15 15.9% 13.1%
2015/16 16.3% 12.6%
2016/17 16.2% 12.5%
2017/18 17.1% 13.0%
2018/19 16.7% 13.7%
2019/20 17.8% 14.0%

4. Finally is there a single variable to flag if anyone in a household is in receipt of income-related benefits? I haven't spotted such a flag so have constructed my own but it would be good to make sure I haven't missed anything.