Data Source
All data in this module comes from the Penn World Table (PWT) version 11.0, published by the Groningen Growth and Development Centre at the University of Groningen.
Citation
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table". American Economic Review, 105(10), 3150-3182.
Variable Definitions
| Variable | PWT Code | Definition | Unit |
|---|---|---|---|
| GDP per capita | rgdpe_pc | Real GDP at chained PPPs (expenditure side) per capita | 2017 US$ PPP |
| Population | pop | Population | Millions |
| Human Capital | hc | Human capital index based on years of schooling and returns to education | Index |
| TFP Level | ctfp | TFP level at current PPPs (USA=1) | Index (USA=1) |
| Labor Share | labsh | Share of labor compensation in GDP at current national prices | Fraction (0-1) |
| Employment | emp | Number of persons engaged | Millions |
Purchasing Power Parity (PPP)
All monetary values are expressed in 2017 US dollars at purchasing power parity (PPP). PPP adjustments account for differences in price levels across countries, making GDP comparisons more meaningful than exchange-rate based comparisons.
For example, a dollar buys more goods and services in India than in Switzerland. PPP adjustments correct for this, providing a better measure of actual living standards.
Growth Accounting
The productivity decomposition uses standard growth accounting based on the Solow growth model:
gY = α · gK + (1-α) · gL + gA
Where:
- gY = GDP growth rate
- gK = Capital stock growth rate
- gL = Labor growth rate (adjusted for human capital)
- gA = TFP growth rate (Solow residual)
- α = Capital share of income (~0.33)
Data Coverage
Data Processing
Raw PWT data is processed through a pipeline that:
- Downloads the official PWT Excel file from the University of Groningen
- Extracts relevant variables (rgdpe, pop, hc, ctfp, labsh, emp)
- Computes derived variables (rgdpe_pc = rgdpe / pop)
- Handles missing values (linear interpolation for small gaps)
- Assigns countries to regions based on World Bank classifications
- Exports to JSON format optimized for web consumption
Enhanced Analysis (v2)
Version 2 introduces additional analysis outputs for deeper exploration of regional growth patterns, distributional dynamics, and economic mobility.
New Data Files
| File | Description | Use Case |
|---|---|---|
| regional-distributions.json | Boxplot stats and KDE data for each region per year | Boxplot/Violin charts |
| decadal-growth.json | Growth rates by country and decade (1960s–2010s) | Small multiples grid |
| mobility-transitions.json | Country quintile transitions 1960→2020 | Sankey diagram |
| regional-trajectories.json | Regional average GDP with confidence bands | Regional comparison chart |
Key Definitions
Quintile Classification
Countries are ranked by GDP per capita and divided into five equal groups (quintiles). Q1 = poorest 20%, Q5 = richest 20%. This classification is computed for both 1960 and 2020 to measure mobility.
Kernel Density Estimation (KDE)
A smoothed approximation of the GDP distribution within each region. Uses Gaussian kernels with Silverman's rule-of-thumb bandwidth selection. Provides richer distributional information than boxplots alone.
Confidence Bands
Regional trajectories show ±1 standard deviation bands around the mean GDP per capita. This captures within-region variation and divergence over time.
Regional Classification
Countries are assigned to 8 major regions for comparative analysis:
Advanced Economies
OECD high-income: USA, Germany, Japan, UK, France, etc.
East Asia
China, South Korea, Taiwan, Hong Kong, Mongolia
South Asia
India, Pakistan, Bangladesh, Sri Lanka, Nepal
Southeast Asia
Thailand, Vietnam, Indonesia, Malaysia, Philippines
Latin America
Brazil, Mexico, Argentina, Chile, Colombia
Sub-Saharan Africa
Nigeria, South Africa, Kenya, Ethiopia, Ghana
Middle East & N. Africa
Saudi Arabia, Egypt, Iran, Turkey, Morocco
Eastern Europe
Poland, Russia, Ukraine, Czech Republic, Romania
Resources
Limitations
- • Historical estimates before 1970 have larger uncertainty margins
- • PPP comparisons are most reliable within income groups
- • TFP is a "residual" and captures measurement error alongside true productivity
- • Some countries have significant data gaps requiring interpolation
- • Recent years (2020+) may be revised as COVID impacts are reassessed