A ~1% sample of individual-level records from the Timor-Leste
Population and Housing Census 2015, prepared for the
occupation-categorisation analysis in Mata Dalan Institute (2020) –
see @source below. Used in
vignette("extract-validate-existing") (Case 1) to
demonstrate recovering an implicit occupation-recoding script as an
explicit crossmap.
Format
A tibble with 11,775 rows and 5 columns:
- houseid
household identifier (5,508 distinct households)
- pno
person number within the household
- p3p3_sex
sex of the individual:
"1. Male"or"2. Female"- p3p4_age
age in years (0-98)
- occupn
original occupation code (161 distinct non-missing values, ranging 110-9999).
NAwhere no occupation code was recorded – these rows skew toward younger ages (median 12 vs. 39.5 for rows with a code) but the two groups overlap, so age alone doesn't fully explain which rows are missing
Source
Individual-level extract of the Timor-Leste Population and Housing Census 2015 microdata, prepared for the occupation-category analysis (Figures 1-2) in: Mata Dalan Institute (2020), "The Informal Sector in Timor-Leste in the Midst of COVID-19", August 2020, with support from Oxfam and Professor Brett Inder (Monash University). https://oi-files-cng-v2-prod.s3.eu-west-2.amazonaws.com/asia.oxfam.org/s3fs-public/file_attachments/MDI_COVID-19_Informal%20sector%20Research_Aug%2020_Final_English.pdf
timor_occupn is a ~1% sample of the full 1,179,654-row individual-level
census extract, grouped by occupn and sampled with dplyr – so the set
of occupation codes present is closer to fully represented than a plain
random sample of individuals would give. See data-raw/occupation.R.