Statistics — PathLens<script> (() => { const root=document.querySelector('[data-stats-longitudinal]'); if(!root) return; const core=JSON.parse(root.dataset.core); const clamp=(v,min,max)=>Math.max(min,Math.min(max,v)); const fmtPct=v=>Number(v).toFixed(1)+'%'; function interpolateSeries(rows, year, key, mode='linear'){ const valid=rows.filter(r=>r[key]!==null&&r[key]!==undefined).sort((a,b)=>a.year-b.year); const exact=valid.find(r=>r.year===year); if(exact) return {value:Number(exact[key]),kind:'OBSERVED',from:year,to:year}; const before=[...valid].reverse().find(r=>r.year<year); const after=valid.find(r=>r.year>year); if(before&&after){ const span=after.year-before.year, part=year-before.year; let value; if(mode==='cagr' && before[key]>0){ const rate=Math.pow(Number(after[key])/Number(before[key]),1/span)-1; value=Number(before[key])*Math.pow(1+rate,part); }else{ value=Number(before[key])+(Number(after[key])-Number(before[key]))*(part/span); } return {value,kind:'DERIVED',from:before.year,to:after.year}; } if(before && year>before.year){ const last=valid[valid.length-1]; const prev=valid[valid.length-2]||valid[0]; const span=Math.max(1,last.year-prev.year); let value; if(mode==='cagr' && Number(prev[key])>0){ const rate=Math.pow(Number(last[key])/Number(prev[key]),1/span)-1; value=Number(last[key])*Math.pow(1+rate,year-last.year); }else{ const delta=(Number(last[key])-Number(prev[key]))/span; value=Number(last[key])+delta*(year-last.year); } return {value,kind:'MODELLED',from:prev.year,to:last.year}; } return {value:Number(valid[0]?.[key]||0),kind:'DERIVED',from:valid[0]?.year,to:valid[0]?.year}; } function statusText(kind){ return kind==='OBSERVED'?'Observed official':kind==='DERIVED'?'Derived interpolation':'Modelled scenario · not forecast'; } function renderDemo(){ const year=+root.querySelector('[data-long="demoYear"]').value; root.querySelector('[data-long-label="demo"]').textContent=year; const rows=core.demographic_history.observed; const keys=['Black African','Coloured','Indian/Asian','White']; let kind='OBSERVED'; const cards=keys.map(key=>{ const x=interpolateSeries(rows,year,key,'linear'); if(x.kind==='MODELLED') kind='MODELLED'; else if(x.kind==='DERIVED'&&kind!=='MODELLED') kind='DERIVED'; return '<article><span>'+statusText(x.kind)+'</span><strong>'+key+'</strong><b>'+fmtPct(clamp(x.value,0,100))+'</b></article>'; }).join(''); root.querySelector('[data-long-output="demo"]').innerHTML=cards; root.querySelector('[data-long-note="demo"]').innerHTML='<strong>'+statusText(kind)+'.</strong> '+(kind==='MODELLED'?core.demographic_history.projection.method:core.demographic_history.note); } function renderIncome(){ const year=+root.querySelector('[data-long="incomeYear"]').value; root.querySelector('[data-long-label="income"]').textContent=year; const rows=core.household_income_history.observed; const mapped=rows.map(r=>({year:r.year,national:r.national,...r.groups})); const keys=['national','Black African-headed households','Coloured-headed households','Indian/Asian-headed households','White-headed households']; const labels={'national':'National average'}; let kind='OBSERVED'; const cards=keys.map(key=>{ const x=interpolateSeries(mapped,year,key,'cagr'); if(x.kind==='MODELLED') kind='MODELLED'; else if(x.kind==='DERIVED'&&kind!=='MODELLED') kind='DERIVED'; return '<article><span>'+statusText(x.kind)+'</span><strong>'+(labels[key]||key)+'</strong><b data-annual-rand="'+Math.round(x.value)+'"></b></article>'; }).join(''); const out=root.querySelector('[data-long-output="income"]'); out.innerHTML=cards; if(window.DemandDesk&&window.DemandDesk.bindAnnualAmounts) window.DemandDesk.bindAnnualAmounts(out); const note=kind==='MODELLED' ? core.household_income_history.projection.method+' '+core.household_income_history.projection.warning : core.household_income_history.note; root.querySelector('[data-long-note="income"]').innerHTML='<strong>'+statusText(kind)+'.</strong> '+note; } function renderAttain(){ const year=+root.querySelector('[data-long="attainYear"]').value; root.querySelector('[data-long-label="attain"]').textContent=year; const rows=core.attainment_history.observed; const defs=[ ['no_schooling','No schooling'], ['at_least_grade12','At least Grade 12'], ['post_school','Post-school education'] ]; let kind='OBSERVED'; const cards=defs.map(([key,label])=>{ const x=interpolateSeries(rows,year,key,'linear'); if(x.kind==='MODELLED') kind='MODELLED'; else if(x.kind==='DERIVED'&&kind!=='MODELLED') kind='DERIVED'; return '<article><span>'+statusText(x.kind)+'</span><strong>'+label+'</strong><b>'+fmtPct(clamp(x.value,0,100))+'</b></article>'; }).join(''); root.querySelector('[data-long-output="attain"]').innerHTML=cards; root.querySelector('[data-long-note="attain"]').innerHTML='<strong>'+statusText(kind)+'.</strong> '+(kind==='MODELLED'?core.attainment_history.projection.method:'Stats SA observed markers are interpolated only when the selected year is not itself an observation.'); } function renderLongitudinal(){renderDemo();renderIncome();renderAttain();} root.querySelectorAll('input[type="range"]').forEach(el=>el.addEventListener('input',renderLongitudinal)); renderLongitudinal(); })(); </script>

Longitudinal explorer

Move through observed history and modelled future scenarios

Observed · Derived · Modelled

Population compositionOfficial Census / Community Survey observations

2022

Average household incomeNominal annual household income; not adjusted for inflation

2023

Compare purchasing power with the inflation calculator →

Educational attainmentPersons aged 20+ · Stats SA General Household Survey

2024

Demographics

Census 2022 population groups

Observed official

Black African

81.4%

Coloured

8.2%

White

7.3%

Indian/Asian

2.7%

What this basically means: national population shares are useful context, but they are not the same thing as the workforce profile of a specific occupation, sector or employer.

Income

Average household income, IES 2022/23

Population-group contextBlack African-headed households

Population-group contextColoured-headed households

Population-group contextIndian/Asian-headed households

Population-group contextWhite-headed households

Income by educational attainment

Observed household averages

AttainmentNo schooling

AttainmentPrimary education

AttainmentSecondary education

AttainmentTertiary education

What this basically means: higher educational attainment is associated with materially higher household income in this dataset, but these are group averages, not guaranteed earnings for an individual.

Labour market · unemployment

Official unemployment rate by population group

2026 Q2 · National 33.6%

2026 Q2 Black/African 37.6%

2026 Q2 Coloured 23.5%

2026 Q2 Indian/Asian 14.7%

2026 Q2 White 9.1%

What this basically means: unemployment differs materially across population groups. These are labour-market outcomes, not Employment Equity targets and not rules for selecting candidates.

Open longitudinal + projected unemployment model →

Inflation