Minimum income questions to estimate subjective poverty lines and examine sub?jective poverty rates, relative to objective poverty rates in South Africa

This research uses minimum income questions to estimate subjective poverty lines and examine subjective poverty rates, relative to objective poverty rates in South Africa. Comparisons of these lines and rates are undertaken over the period 2014 to 2023, based on two different nationally representative surveys. We find that subjective poverty lines per adult and child increased between 2014 and 2023. We also find per capita subjective values in-line with South Africa's upper bound money-metric poverty line in 2023, but not for 2014. Furthermore, if the 2014 estimates are adjusted to account for inflation, they would be larger than the subjective povery lines estimated for 2023 in many cases. Despite some similarities, we find that subjective/objective poverty rates do not coincide for any year or household type, suggesting more work is needed to develop consistent poverty lines for household types at any point in time, and preferably, over time.The data is taken from Stats-SA, but housed at DataFirst. Statistics South Africa. 2017. “Living Conditions Survey 2014–2015 [Dataset].” Pretoria; Cape Town: Statistics South Africa [producer]; DataFirst [distributor]. https://doi.org/10.25828/9229-xz60. Statistics South Africa. 2023. “General Household Survey 2023 [Dataset].” Pretoria; Cape Town: Statistics SA [producer]; DataFirst [distributor]. https://doi.org/10.25828/9khq-h317. The "R" code takes these datasets and develops all the results presented in the paper.<br>

Institutions

Publication Details

Journal
Figshare
Published
2025-09-30
DOI
https://doi.org/10.25403/upresearchdata.30225631.v1
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Minimum income questions to estimate subjective poverty lines and examine sub?jective poverty rates, relative to objective poverty rates in South Africa

Figshare
Image Processing and 3D Reconstruction
article

Minimum income questions to estimate subjective poverty lines and examine sub?jective poverty rates, relative to objective poverty rates in South Africa

article en

Abstract

This research uses minimum income questions to estimate subjective poverty lines and examine subjective poverty rates, relative to objective poverty rates in South Africa. Comparisons of these lines and rates are undertaken over the period 2014 to 2023, based on two different nationally representative surveys. We find that subjective poverty lines per adult and child increased between 2014 and 2023. We also find per capita subjective values in-line with South Africa's upper bound money-metric poverty line in 2023, but not for 2014. Furthermore, if the 2014 estimates are adjusted to account for inflation, they would be larger than the subjective povery lines estimated for 2023 in many cases. Despite some similarities, we find that subjective/objective poverty rates do not coincide for any year or household type, suggesting more work is needed to develop consistent poverty lines for household types at any point in time, and preferably, over time.The data is taken from Stats-SA, but housed at DataFirst. Statistics South Africa. 2017. “Living Conditions Survey 2014–2015 [Dataset].” Pretoria; Cape Town: Statistics South Africa [producer]; DataFirst [distributor]. https://doi.org/10.25828/9229-xz60. Statistics South Africa. 2023. “General Household Survey 2023 [Dataset].” Pretoria; Cape Town: Statistics SA [producer]; DataFirst [distributor]. https://doi.org/10.25828/9khq-h317. The "R" code takes these datasets and develops all the results presented in the paper.<br>

Figshare
University of Pretoria (ZA)
No poverty
Openalex Percentile: Top 45%
Image Processing and 3D Reconstruction
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.