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# Code for checking the helpbuy questions on W12 hhresp file
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# Libraries (install first as required)
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packages <- c("tidyverse", "haven", "lubridate", "descr")
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lapply(packages, library, character.only = T)
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rm(packages)
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# Load W12 hhresp from computer (adapt file path below first)
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UKHLS_W12_hhresp <- read_dta(file = "./Datasets/SN6614/UKDA-6614-stata/stata/stata13_se/ukhls/l_hhresp.dta") %>%
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mutate_if(haven::is.labelled, haven::as_factor)
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# Retain the variables we need for checking the issue
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UKHLS_test <- select(UKHLS_W12_hhresp,
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l_hidp, l_ivfho, l_origadd,
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l_intdatem, l_intdatey,
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l_hholdmodedv,
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l_hsownd,
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starts_with("l_helpbuy"))
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# Helpbuy questions are routed to owners only - so drop everyone else
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table(UKHLS_test$l_hsownd, UKHLS_test$l_helpbuy1, useNA = "ifany")
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UKHLS_test <- filter(UKHLS_test,
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l_hsownd %in% c("Owned outright",
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"Owned/being bought on mortgage",
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"Shared ownership (part-owned part-rented"))
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# No obvious tenure patterning in inapplicable codes on helpbuy variables
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table(UKHLS_test$l_hsownd, UKHLS_test$l_helpbuy1, useNA = "ifany")
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# Note - number of inapplicable/refusal/DK is constant across helpbuy variables
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# Generate interview date indicator to explore whether dates are part of the
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# missing data puzzle
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UKHLS_test <- mutate(UKHLS_test,
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intdate = my(paste(as.character(l_intdatem),
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as.character(l_intdatey),
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sep = " ")))
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# Examine helpbuy pattern by interview time
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crosstab(UKHLS_test$intdate, UKHLS_test$l_helpbuy1, plot = F, prop.r = T)
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# Note - can see the big jump in 'inapplicable' codes at Jan 2021. Prior to Jan '21
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# around 20% were inapplicable each month but from Jan this is 70+%
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# Now examine whether interview mode has a bearing on this
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table(UKHLS_test$l_hholdmodedv, useNA ="ifany")
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crosstab(UKHLS_test$l_hholdmodedv, UKHLS_test$l_helpbuy1, prop.r = T,
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plot = F) # Overall higher NA from CAWI, middle CATI, lowest CAPI
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capi <- filter(UKHLS_test, l_hholdmodedv == "capi")
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crosstab(capi$intdate, capi$l_helpbuy1, prop.r = T, plot = F)
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# Low n CAPI in this wave due to COVID
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cati <- filter(UKHLS_test, l_hholdmodedv == "cati")
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crosstab(cati$intdate, cati$l_helpbuy1, prop.r = T, plot = F)
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# Percentage of CATI respondents with inapplicable values is low until Feb 21
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# then increases hugely
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cawi <- filter(UKHLS_test, l_hholdmodedv == "cawi")
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crosstab(cawi$intdate, cawi$l_helpbuy1, prop.r = T, plot = F)
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# Sudden jump in inapplicables from ~20% in 2020 to >80% in January 2021.
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# Overall conclusion - through 2020 most owner respondents did report values
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# on helpbuy. However, from 2021, across CATI and CAWI we see a switch to
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# much higher % inapplicable. The reason for this is unclear.
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