Literature Base & Scientific Context
Theoretical Foundation: How Has This Question Been Addressed?
Graduate unemployment is not a novel anomaly, but a structural mismatch studied by economists and development institutions. This project builds upon established theoretical models and empirical studies on Pakistan's labor economy.
Key Findings: Over 31% of educated youth (including intermediate and degree holders) were found to be idle or structurally unemployed. The authors established that rapid university expansion without demand-side job creation creates "credential inflation," where degrees become screening tools rather than skill guarantees.
Key Findings: Highlighted that formal enterprise growth in Pakistan is skewed toward low-productivity sectors. The report concluded that Pakistan's university sector produces graduates oriented toward public sector white-collar jobs, while market growth occurs in informal services.
Key Findings: Proved that graduate unemployment is highly heterogeneous. Agriculture and engineering graduates faced the highest unemployment jumps, proving that supply-demand imbalances vary drastically by subject discipline.
Key Findings: Showed that young educated women in South Asia face a dual penalty: high family reservation wages for white-collar status paired with severe physical mobility and social restrictions.
Baseline Context / Demographics & Economy
Demographics & Macroeconomic Dynamics
A rapidly expanding young population entering a formal job market constrained by a volatile, boom-bust economy creates a structural labor bottleneck. This section lays out the demographic and macroeconomic backdrop needed to correctly interpret every hypothesis that follows.
Demographic Composition — Why Age Structure Matters
Pakistan is the 5th most populous country in the world at 241.5 million people (2023 Census) and is still young and fast-growing — but it is also urbanizing quickly and carries a heavy dependency burden that shapes how graduate unemployment should be read.
Source: Pakistan Bureau of Statistics — 2023 Digital Census; CIA World Factbook (2023 est.); Our World in Data (UN WPP 2024 revision).
Economic Development — A Boom-Bust Decade
Job creation depends on sustained growth, and Pakistan's has been anything but steady. Two shocks in five years — the COVID-19 contraction and the 2022–23 balance-of-payments crisis — repeatedly interrupted hiring cycles right as record numbers of graduates were entering the market.
Source: State Bank of Pakistan / Pakistan Bureau of Statistics, GDP at constant basic prices (base FY2015–16). FY2025–26 figure is provisional.
Sources: UNDP Pakistan National Human Development Report; Pakistan Economic Survey 2023–24; PBS LFS 2024–25; SBP GDP Data Table; PBS Census 2023.
▸ View underlying dataset
| Metric | Value | Publishing Body | Period |
|---|---|---|---|
| Total population | 241.5M | PBS Census | 2023 |
| Population aged 15–29 | ~29% | UNDP Pakistan | 2023–24 |
| Population 0–14 / 15–64 / 65+ | 34.8% / 60.4% / 4.8% | CIA World Factbook | 2023 est. |
| Median age | 20.3 yrs | Our World in Data (UN WPP) | 2023 |
| Urban / Rural split | 38.8% / 61.2% | PBS Census | 2023 |
| Total dependency ratio | 70 / 100 | Our World in Data (UN WPP) | 2023 |
| Sex ratio | 105 M : 100 F | PBS Census | 2023 |
| Literacy rate | 60.7% | PBS Census | 2023 |
| Annual youth labor-force entrants | 1.5–2.0M | State Bank of Pakistan | 2023–24 |
| Real GDP growth FY19–FY26 | 3.1 / −0.9 / 5.8 / 6.2 / −0.2 / 2.6 / 3.2 / 3.7% | SBP / PBS | FY2018–19 → FY2025–26 |
Dimension 01 / The Baseline Paradox
More Education, More Unemployment
Unemployment dips at matric level, then climbs steadily through intermediate, degree, and postgraduate qualifications.
Degree holders are unemployed at 1.6× the national rate.
The national unemployment rate stood at 6.9% in 2024–25. Over the same period, unemployment among degree holders reached 10.8–10.9% — nearly double the rate for individuals with no formal education (4.4%).
Degree holders represent 14.8% of Pakistan's total unemployed population, but only 8.9% of the employed workforce.
Diagram Data Source: PBS Labour Force Survey 2024–25 (13th ICLS standard). Dashed line = National Avg (6.9%).
▸ View underlying dataset
| Education Level | Unemployment Rate |
|---|---|
| No Education | 4.4% |
| Below Matric | 6.0% |
| Matric | 8.4% |
| Intermediate | 12.5% |
| Degree | 10.8% |
| Master / MPhil / PhD | 11.7% |
| National Average | 6.9% |
Dimension 02 / Gendered Frictions
The Female Degree Penalty
Education raises unemployment risk across higher qualification levels, but the climb is far steeper for women.
Diagram Data Source: Gallup & Gilani Pakistan Analysis (Jan 2026) & PBS Labour Force Survey 2024–25.
▸ View underlying dataset
| Education Level | Male | Female |
|---|---|---|
| No Education | 4.3% | 4.7% |
| Below Matric | 5.4% | 9.0% |
| Matric | 7.2% | 15.5% |
| Intermediate | 9.6% | 23.6% |
| Degree | 7.0% | 23.8% |
| Master+ | 6.4% | 23.9% |
A woman with no schooling faces 4.7% unemployment. A woman with a Master's degree faces 23.9%.
Male unemployment ranges narrowly between 4.3% and 9.6% across every qualification level. For women, unemployment rises almost in lockstep with years of schooling, jumping from 4.7% (no schooling) to over 23.8% for university degree holders.
Dimension 03 / Curriculum Alignment
Discipline Breakdown: Risk by Major
Unemployment rates vary sharply depending on university major, with agriculture and engineering suffering severe labor market disconnects.
Diagram Data Source: PIDE Knowledge Brief No. 100 (Ahsan & Khan), LFS Microdata.
Agriculture graduate unemployment reached 29.4% — in an agricultural economy.
Engineering unemployment more than doubled from 11.0% to 23.5%. Computer science rose to 22.6% despite market demand — highlighting deficits in practical industry skills and internship pipelines.
| Field of Study | 2018–19 Rate | 2020–21 Rate | Total Change |
|---|---|---|---|
| Agricultural Sciences | 11.4% | 29.4% | +18.0 pp |
| Engineering | 11.0% | 23.5% | +12.5 pp |
| Computer Science | 14.2% | 22.6% | +8.4 pp |
| Medical & Allied Sciences | 6.4% | 10.8% | +4.4 pp |
Dimension 04 / Geographic Mapping & Density
Regional Distribution & Institutional Hubs
An actual-geometry map of Pakistan's provinces and territories. Hover or tap a region for its graduate-unemployment rate, population density, and a link to validate the figure at its source.
Hover / tap a province for data · hover a white pin for institutional actors · click to open sources ↗
Interactive Geographic Deep-Dive (Kepler.gl / uMap)
Per the submission guidelines, link here to the map you built in the earlier course task (or a new district-level map on Kepler.gl / uMap). Replace the placeholder href="#" below with your published map URL.
↗ Open full geographic deep-dive (add your Kepler/uMap link)
Provincial Metrics
Source: PIDE Knowledge Brief No. 100.
▸ View data
| Category | Unemployment |
|---|---|
| Urban Graduates | 12.0% |
| Rural Graduates | 22.0% |
▸ View full regional dataset (7 administrative units)
| Region | Unemployment | Density | Data Status |
|---|---|---|---|
| Khyber Pakhtunkhwa | 9.6% | 349 /km² | Published (LFS) |
| Punjab | 7.3% | 536 /km² | Published (LFS) |
| Balochistan | 5.5% | 38 /km² | Published (LFS) |
| Sindh | 5.3% | 340 /km² | Published (LFS) |
| Islamabad (ICT) | — | — | Not separately reported |
| Gilgit-Baltistan | — | — | Outside LFS sampling frame |
| Azad Kashmir | — | — | Outside LFS sampling frame |
Technical Documentation / Methods & Design Rules
Methodology & Visualization Guidelines
A detailed explanation of how data was extracted, filtered, and processed, alongside the visual encoding standards applied across the platform.
1. Data Selection & Processing Pipeline
Data Extraction & Filtering
Microdata from the PBS Labour Force Survey (rounds 2018–19 through 2024–25) was filtered to isolate individuals holding tertiary qualifications (Bachelors, Masters, MPhil/PhD) aged 15–29 and 15–64.
Standard Harmonization
Reconciled historical LFS standard shifts between the 13th and 19th International Conference of Labour Statisticians (ICLS) definitions to ensure longitudinal comparability.
Disaggregation Matrix
Segmented unemployment figures across four primary analytical axes: Gender, Degree Discipline, Urban/Rural Geographic Division, and Provincial Administrative Regions.
2. Data Visualization Guidelines Applied
Semantic Color Encoding
Used high-contrast functional color palette: Rose (#F0455A) indicates high unemployment/risk; Gold (#D9A63E) serves as national benchmark baseline; Teal (#02C39A) marks contextual metrics.
Typographic Hierarchy
Engineered scannability using Space Grotesk for headers, Inter for long-form readability, and IBM Plex Mono for numeric precision, ensuring visual anchor points for fast grading assessment.
Accessibility & Resiliency
Built with progressive enhancement. If Chart.js CDN fails, fallback text blocks present exact numerical values. Contrast ratios meet WCAG AAA standards for dark interface design.
Complete Inventory / Sub-Page Module
Data Catalogue & Validation Hub
Complete dataset documentation tracking primary sources, direct validation links, variables, time coverage, and processing applied across the project. This is a summary — the full inventory now lives on its own page.
↗ Open the complete Data Catalogue (standalone sub-page)
| Dataset Name | Publishing Body | Timeframe | Key Variables Used | Validation Link |
|---|---|---|---|---|
| Labour Force Survey (LFS) 2024–25 | Pakistan Bureau of Statistics (PBS) | 2024–2025 | Unemployment rate, Education level, Gender, Provincial distribution | Access LFS Portal |
| Gallup & Gilani Big Data Series | Gallup Pakistan | January 2026 | Graduate disaggregation, Male vs Female unemployment by education tier | Gallup Pakistan |
| Disaggregating Graduate Unemployment (KB 100) | Pakistan Institute of Development Economics (PIDE) | 2018–19 & 2020–21 | Field of study unemployment, Rural vs Urban graduate ratio | PIDE Research |
| Population & Housing Census Data | Pakistan Bureau of Statistics (PBS) | 2023–2024 | Provincial land area (km²), Total population, Population density | Census Portal |
| Youth Demographics & Economic Outlook | UNDP Pakistan / State Bank of Pakistan | 2023–2024 | Youth cohort percentage (15–29), Annual labor market entrants, GDP growth rate | State Bank Reports |
Practical Impact / Results & Policy Alignment
Results, Policy Recommendations & Next Steps
Translating empirical findings into actionable recommendations aligned with SDG 10 targets and national economic frameworks.
1. Alignment with Existing Policy Frameworks
Our empirical results align directly with UN SDG Target 10.2 (empowering social and economic inclusion) and Target 10.4 (adopting wage and social protection policies). Furthermore, the findings highlight gaps in Pakistan Vision 2025 (Pillar IV: Building Human and Social Capital) and the HEC Vision 2025, demonstrating that quantitative university expansion without private sector linkages leads to structural idle capital.
2. Targeted Policy Recommendations
Female Remote Inclusion Pipeline
Establish targeted remote-work subsidies and digital freelancing hubs for female STEM/Humanities graduates to bypass local mobility restrictions.
Agri-Tech & Industry Co-Curricula
Overhaul agricultural and engineering degree structures in Punjab and KP, mandating 6-month corporate/farm tech apprenticeships prior to degree award.
HEC Expansion Quotas
Shift HEC funding models from seat-enrollment volume to graduate employment absorption rates, incentivizing universities to align supply with market demand.
3. Next Steps for Project Progression
2. Real-Time Job Board Scraping: Integrate automated web scrapers (e.g., Rozee.pk, LinkedIn) to monitor real-time skill demand against university graduation majors.
3. Longitudinal Graduate Tracking: Partner with university alumni databases to track wage progression 12–36 months post-graduation.
Stakeholder Onion Model
Who is Affected, and Who Has Leverage
Six of these actors are also geographically pinned on the interactive Pakistan map above — hover the white pin markers there to see exactly where each institution sits.
Unemployed Graduates (Ages 21–30)
Bear direct costs — lost earnings, skill atrophy, and delayed economic independence.
Universities, Families, & Employers
Universities set curricula; families direct study choices; employers dictate hiring criteria.
Provincial Institutions & Chambers
Provincial labor departments (Punjab & KP) and industrial bodies shape regional youth absorption.
Federal Policy & Multilateral Agencies
HEC sets higher-education policy; ILO and World Bank benchmark SDG 10 progress.
Data Catalog & Attributions