THE DATA

Data & Methodology

How the simulations work, what data they use, and the assumptions behind every projection.

How the simulation works

Scenario modelling

Each model calculates economic outcomes under adjustable policy assumptions. Sliders represent policy variables — tax rates, investment levels, reform speed. The models show what is possible under a given set of choices, not what will happen.

Not a forecast

These are scenario tools, not econometric forecasts. They do not model second-order effects, political risk events, or global macro shocks. They answer one question: if these inputs are true, what do the outputs look like?

How to use it

Set the sliders to your assumptions and read the outputs. Conservative on political stability? Drag it to 3. Optimistic on FDI? Set it to 9. The model responds to your assumptions, not ours.

Pakistan baseline data — 2024

The base figures all models are calibrated against.

Indicator Value Source Year
GDP (nominal USD)$374BWorld Bank2024
GDP (PKR)PKR 104.7TState Bank of Pakistan2024
GDP growth rate2.4%IMFFY24
Population240 millionUN Population Division2024
Population growth rate2.0% / yrPBS2024
Median age22 yearsUN2024
Labour force~72 millionPBS2024
Unemployment (official)6.3%PBSFY24
Unemployment (incl. underemployment est.)~22%ILO / analyst estimates2024
FBR tax collectionPKR 9.31 trillionFBR Year BookFY24
Tax-to-GDP ratio~9%IMF2024
Active taxpayers (ATL)~5.6 millionFBR Active Taxpayer List2024
SECP registered companies250,000+SECP Annual Report2024
Literacy rate58%PBS / UNICEF2023
Out-of-school children~26 millionUNICEF2024
Installed power capacity~45 GWNEPRA2024
Actual dispatched capacity~22 GWNEPRA / NTDC2024
Circular debtPKR 2.6 trillionPower Division2024
Average electricity tariffPKR 50–65/kWhNEPRA2024
Agriculture share of GDP~24%PBSFY24
Manufacturing share of GDP~20%PBSFY24
Services share of GDP~56%PBSFY24
Export value (goods)~$25BSBPFY24
Import value (goods)~$54BSBPFY24
Current account deficit~$1.6BSBPFY24
Remittances$27BSBPFY24
Foreign exchange reserves~$9B (SBP)SBP2024
External debt~$124BSBPFY24
PKR/USD exchange rate~280SBP / market2024
Medical graduates per year~25,000PMC2024
Pharmaceutical exports~$700MPBSFY24
IT / freelance exports~$400M+P@SHA / SBPFY24
International tourists~500,000PTDC2023
Agricultural exports~$3.5BPBSFY24

Model methodology

Click any model to expand the full methodology, variables, and assumptions.

Model 1 — Tax & Fiscal Reform
Key formula: total = (smallBiz × revSmall × 5%) + (bigBiz × revLarge × 9%) + (imports × 5%) + (companies × PKR25K) + (visas × PKR75K)
Variables: Number of registered businesses, % above PKR26M revenue threshold, avg revenue per tier, import volume, visa issuances
Assumptions: PKR/USD = 280; company renewal compliance = 100%; VAT replaces all customs duties revenue-neutrally at the border; tax compliance improves linearly with digital enforcement
Limitations: Does not model VAT evasion, informal economy absorption rate, or compliance curve shape
Model 2 — Fintech & Business
Key formula: total = carVAT + importVAT + bizTax × adoptionRate + fintechLevy + propStampDuty
Variables: Tech adoption speed (1–10 proxy), car import volume, car price, new business registrations, property value multiplier, fintech transaction volume
Assumptions: Car price reduction proportional to duty removal; fintech levy = 0.1% of digital transaction volume; property stamp duty = 2% on total property market × adoption rate
Model 3 — Automotive & Trade
Key formula: total = carVolK × carPricePKR × 5% + importVAT + autoDealers × avgRev × 5% × adoptionRate
Variables: Annual car import volume (K units), average car price (M PKR), auto dealer count, finance adoption rate, digital transaction volume
Assumptions: Market adoption rate proxies compliance and formalisation speed; car market size grows proportionally with duty removal; auto dealer revenue estimated at PKR 8M average
Model 4 — CPEC & Geo-Economic Corridor
Key formula: revenue = (transitVolume × PKR_RATE × 2%) + (gwadarBase × gwadarMult)
Variables: China-Pakistan trade volume ($B), transit cargo volume ($B), CPEC infrastructure investment, Gwadar port throughput multiplier
Assumptions: 2% transit levy achievable under bilateral treaty; Gwadar base revenue = PKR 2T; CPEC GDP contribution = 5% of trade volume × 5-year multiplier
Model 5 — Tourism & Construction
Key formula: totalYr5 = (intlRevYr1 + domRevYr1) × (1 + tGrowth)^4
Variables: International tourist arrivals, average spend (USD), nights stayed, domestic tourists, growth rate, hotel/resort construction pipeline
Assumptions: PKR/USD = 280 constant; tourism growth exponential from low base; construction cost estimates based on regional benchmarks; government captures 13% via VAT and tourism levy
Model 6 — Agriculture
Key formula: agriGDP = base × (1 + yieldBoost×0.6) × (1 + areaExpansion/50) × 1.08^4
Variables: Yield improvement %, new cultivated area (M ha), export growth rate, food processing multiplier, cold chain coverage %, water efficiency %
Assumptions: Base agricultural GDP = PKR 28T; base agri exports = $3.5B × PKR_RATE; yield uplift translates to 60% of stated % due to infrastructure and market absorption constraints
Model 7 — Manufacturing & SEZs
Key formula: mfgGDP = base × (1 + growthRate)^4 × (1 + sezFDI/500T)
Variables: SEZ FDI inflow ($B), textile upgrade %, electronics plants, pharma growth %, auto units, SEZ count, workers per SEZ, manufacturing growth rate
Assumptions: Base manufacturing GDP = PKR 24T; SEZ FDI multiplier assumes 35% of FDI converts to annual output; textile upgrade captures full value-chain from raw to finished goods
Model 8 — Energy Reform
Key formula (tariff): newTariff = max(18, 58 − (solarYr5×0.4 + tdLoss×0.8 + debtRes×15))
Key formula (load-shedding): loadShedding = max(0, 11 − (renewYr5×0.15 + tdLoss×0.3 + debtRes×4))
Assumptions: Base installed = 45GW; base dispatched = 22GW; circular debt = PKR 2.6T; baseline tariff = PKR 58/kWh; baseline load-shedding = 11 hrs/day
Model 9 — Education & Human Capital
Key formula (literacy): literacyYr5 = min(95, 58 + girls×5×0.8 + budgetBoost×25 + (schools/50000)×10)
Key formula (ROI): totalROI = gdpUplift + itExports + diasporaRemittances + tvetProductivity
Assumptions: Base literacy = 58%; 1 school serves 15 students; IT export base = $400M with linear growth from digital workforce expansion
Model 10 — Healthcare & Medical Tourism
Key formula: total = healthGDP + medTourRevenue + pharmaRevenue
Variables: Health budget % of GDP, hospitals per year, international hospitals, medical tourists, spend per patient (USD), pharma export growth %, telemedicine reach, doctor retention %
Assumptions: Base health GDP grows at 12%/yr under increased spending; medical tourism revenue per patient = medSpend × 5 visits × PKR_RATE; base pharma exports = $700M × PKR_RATE
Model 11 — Real Estate & Property
Key formula: total = mortgageRev + rentalRev + stampDuty + fdiPKR × 0.05
Variables: Total property value (T PKR), real estate FDI ($B/yr), mortgage adoption %, rental yield %, REIT listings, development projects
Assumptions: PKR/USD = 280; stamp duty = 5% on total property market value × (devProjects/100); mortgage revenue = propertyValue × mortgageAdoption% × 2%; REIT market cap = listings × PKR 20B × PKR_RATE

Key assumptions

  • PKR/USD exchange rate held constant at 280 throughout all projections
  • Population growth not explicitly modelled in per-capita output calculations
  • Political stability modelled as a continuous multiplier (0.1–1.0), not a binary event risk
  • Sector interactions modelled as additive contributions, not multiplicative feedback loops
  • All projections assume reform implementation begins in Year 1 with no lag
  • Compliance rates assumed to improve linearly with digital enforcement infrastructure
  • Global macro conditions (oil prices, US interest rates, China growth) held constant
  • Climate and water stress modelled only via the water efficiency variable in Model 6
  • Models do not capture second-order effects (e.g. energy reform enabling manufacturing growth)

Limitations

  • These are scenario tools, not econometric forecasts or investment recommendations
  • Real outcomes will be shaped by political implementation, which is not modelled
  • Interaction effects between sectors are likely larger than additive modelling suggests
  • Informal economy formalisation speed is highly uncertain and varies by sector
  • Infrastructure quality and institutional capacity are proxied but not fully modelled
  • International market conditions for Pakistani exports are assumed favourable

Primary data sources

The publicly available sources underlying all model calibrations.

World Bank Open Data
GDP, poverty, human development indicators
International Monetary Fund (IMF)
Macroeconomic projections, Article IV consultations
Federal Board of Revenue (FBR)
Tax collection, active taxpayer list, FBR Year Book
State Bank of Pakistan (SBP)
Monetary data, remittances, external sector statistics
Pakistan Bureau of Statistics (PBS)
Labour force survey, GDP by sector, census data
NEPRA
National Electric Power Regulatory Authority — power capacity, tariff, loss data
SECP
Securities & Exchange Commission — company registrations, corporate data
Pakistan Telecom Authority (PTA)
Digital economy, broadband penetration data
UN Population Division
Demographic projections, median age, workforce data
International Labour Organization (ILO)
Employment, informal economy estimates
UNICEF Pakistan
Education statistics, out-of-school children data
PTDC
Pakistan Tourism Development Corporation — tourist arrivals data
P@SHA
Pakistan Software Houses Association — IT exports, freelancing data
Power Division, Ministry of Energy
Circular debt data, CPEC energy projects

Download

Raw data files and full methodology documentation — available at launch.

Coming soon — available at launch
Coming soon — available at launch
Coming soon — available at launch

All data used in this simulation is publicly available from the sources listed above. The simulation models are available for review on GitHub.

DATA SOURCES

References & Bibliography

All data, statistics, and institutional figures used across the Pakistan Unleashed simulation platform and book. APA 7th Edition. Every figure in the simulation is traceable to a primary source below.

71 total sources 23 international organisations 23 Pakistan government bodies 4 industry & CPEC 6 regional comparisons 3 energy & climate 3 agriculture 3 healthcare & pharma 6 trade, diaspora & education
23

International Organisations

23 sources

23

Pakistan Government and Regulatory Bodies

23 sources

4

Industry Associations and CPEC

4 sources

6

Regional and Comparative Data

6 sources

3

Energy and Climate

3 sources

3

Agriculture

3 sources

3

Healthcare and Pharmaceuticals

3 sources

6

Trade, Diaspora and Education

6 sources

NOTE ON DATA ACCURACY

All figures marked with (~) are approximate estimates based on the most recently available official data. Where official figures conflict across sources, the more conservative estimate has been used. All PKR figures use the exchange rate of PKR 280 per USD unless otherwise stated. Fiscal year (FY) in Pakistan runs July 1 - June 30. This reference list reflects data available as of April 2026. Some URLs may require navigation through the source institution's website if direct links have changed. Citation format: APA 7th Edition.