Understanding South Africa’s labour market is essential for students, career advisors and jobseekers who want to choose careers with real demand, benchmark salaries, or make evidence-based decisions. This guide lists the best downloadable datasets, explains how to visualise them for career research, and gives practical steps you can follow today.
Why official microdata matters for career guidance
- Official household surveys give the most reliable, nationally representative view of employment, unemployment, industry change and earnings.
- Microdata (unit‑record files) lets you build occupation-level, provincial and age-group indicators that off‑the‑shelf charts rarely show.
Key datasets and distribution platforms below make those microdata files available for download and analysis. (isibaloweb.statssa.gov.za)
Core downloadable datasets (what to get and why)
| Dataset | Producer / Host | Typical formats | Best use for career research |
|---|---|---|---|
| Quarterly Labour Force Survey (QLFS) | Statistics South Africa (Stats SA) — QLFS pages & releases | CSV, SPSS, STATA, SAS | Quarterly employment/unemployment rates, occupation counts (OFO), provincial comparisons — ideal for up‑to‑date demand signalling. (statssa.gov.za) |
| Labour Market Dynamics (LMD / annualized) | Statistics South Africa (LMD combines QLFS quarters) | CSV / metadata bundles | Earnings and income variables that aren’t always in quarterly files — useful for salary benchmarks and median wages by occupation. (isibaloweb.statssa.gov.za) |
| Unit-record microdata collections (QLFS historic stacks & PALMS) | DataFirst / World Bank microdata libraries | DDI + flat files (CSV, STATA, SPSS) | Long‑run series, comparability across years, research on occupational trends and structural change (PALMS stacks multiple surveys). (datafirst.uct.ac.za) |
| SETA Sector Skills Plans & Scarce/Critical Skills lists | Individual SETAs (e.g., MICT, merSETA) | PDF, Excel | Sector-specific priority/shortage lists and Top‑10 occupation priorities — useful to triangulate which occupations employers signal as scarce. (mict.org.za) |
| Critical Skills List (work‑visa relevant) | Department of Home Affairs (gazetted list) | PDF (Government Gazette) | Confirms nationally declared “critical” occupations — useful for graduates considering international hires or foreign recruiters. (dha.gov.za) |
Note: QLFS unit records are published per quarter on the Stats SA portal (downloadable CSV/SPSS/STATA), while consolidated and historical microdata are available from DataFirst and global microdata libraries. (isibaloweb.statssa.gov.za)
Practical visuals to build for career advice
Focus your dashboards on the decision-makers you support (students, mid-career changers, employers). Useful visual types:
- Occupation demand bar chart (headcount by OFO 3-digit, latest quarter & YoY change).
- Provincial heatmap of vacancy‑equivalent / employment growth (where jobs are concentrated).
- Age‑by‑occupation pyramid (shows retirements / future replacement needs).
- Salary distribution boxplots (median, IQR by occupation & experience).
- Time series: unemployment rate, employment-to-population ratio, and sector employment over time (to spot structural decline/growth).
- Scatter plot: median salary vs. employment growth (identify high‑paying growing occupations).
Visualisation guidance and tools below show how to implement these quickly. For design best practice and interactive publishing, consider Datawrapper or Tableau Public. (datawrapper.de)
Tools & quick workflow (download → clean → visualise)
- Download:
- Grab the latest QLFS quarter CSV and the corresponding LMD earnings file from the Stats SA ISIbalo/Q LFS pages. (isibaloweb.statssa.gov.za)
- For historical panels, retrieve PALMS or DataFirst stacks. (datafirst.uct.ac.za)
- Prepare:
- Map occupational codes to a consistent OFO level (3‑digit or 4‑digit) across quarters.
- Reweight if combining quarters (follow Stats SA weighting notes in the metadata). (isibaloweb.statssa.gov.za)
- Clean earnings (impute or trim top‑codes if needed) and create experience/age cohorts.
- Visualise:
- Start with Datawrapper (fast, accessible, shareable) or Tableau Public for interactive dashboards. For reproducible research, use R (tidyverse + ggplot2 / plotly) or Python (pandas + plotly). (datawrapper.de)
- Publish & share:
- Host interactive dashboards (Tableau Public, Datawrapper) and provide CSV/JSON downloads so career centres and students can reuse the outputs.
Tips for career guidance research (what to extract)
- Top demand occupations by count and by growth rate.
- Median and 25/75th percentile earnings by occupation & experience (use LMD for earnings). (isibaloweb.statssa.gov.za)
- Provincial comparisons — which provinces show fastest job creation in a given sector (use QLFS geography). (isibaloweb.statssa.gov.za)
- SETA signals — cross-check your occupation shortlist with SETA Sector Skills Plans and scarce skills lists to prioritise training pathways. (mict.org.za)
- Critical Skills List check — if advising students targeting employer-sponsored visas or international hires, verify whether an occupation appears on the Home Affairs list. (dha.gov.za)
Example small dashboard data model (columns to prepare)
- quarter, year, province, ofo_code, occupation_name, employed_count, unemployed_count, median_monthly_earnings, mean_monthly_earnings, age_group, education_level, sector_code
Where employers and researchers source additional demand signals
- SETA SSPs and scarce/critical skills reports (sector-level demand). (mict.org.za)
- Job-posting aggregators and proprietary labour analytics (used by recruiters), which can supplement official stats for near‑real‑time demand signals. When possible, triangulate with official QLFS numbers. (oecd.org)
Recommended reading (deepen your analysis)
- For occupation priority lists and a data‑driven view of demand: Career Guidance South Africa: Top Demand Occupations 2026 — Data from Stats SA and SETAs.
- To align provincial career advice to local labour markets: Provincial Skill Shortages in South Africa: Where Jobs Are Growing and Which Skills to Learn.
- For salary benchmarking and building pay guidance: South Africa Salary Benchmarks: How Much You Should Earn by Role and Experience.
- To frame unemployment risks and opportunities for students: Analysing Unemployment Trends in South Africa: Implications for Jobseekers and Students.
- Visa and hiring implications of the government list: Critical Skills List Explained: What It Means for Work Visas and Local Hires in South Africa.
- How to turn data into career choices: How to Use Labour Market Data to Choose a High-Demand Career in South Africa.
- Build interactive salary tools: Interactive Salary Calculator for South African Occupations — Build Your Own Benchmark.
- Sector hiring outlooks: Industry Outlooks: Which Sectors Will Hire Most in South Africa Over the Next 5 Years?.
- Employer use of SETA/Stats SA in recruitment: How Employers Use SETA and Stats SA Data in Recruitment — A Guide for Jobseekers.
Final checklist before you publish or advise
- Did you use the latest QLFS quarter and the corresponding LMD file for earnings? (QLFS files and LMD are published on Stats SA’s portal and DataFirst.) (isibaloweb.statssa.gov.za)
- Did you harmonise OFO codes and apply correct weights when stacking quarters? (follow the metadata/guide included with each Stats SA download). (isibaloweb.statssa.gov.za)
- Did you triangulate occupation demand with SETA Sector Skills Plans and the Department of Home Affairs Critical Skills List for visa/shortage context? (mict.org.za)
If you’d like, I can:
- produce a starter R or Python notebook that downloads QLFS/ LMD and builds the five visuals listed above; or
- create a simple Datawrapper dashboard template (CSV → map, bar, and boxplot) you can reuse in career workshops.
Which output would help you most right now — a reproducible notebook or a Datawrapper/Tableau template?