Data catalogue
17 primary data sources spanning migration, climate, economy, governance, conflict, demography, gravity, policy, socioeconomic projections and physical displacement. All combined into a 316,020-observation dyad-period panel (230 countries × 52,670 bilateral corridors × 6 historical periods, 1990–2015) with 99.57% completeness on core predictors.
- 316,020 directed dyad-period observations
- 230 countries · 52,670 bilateral corridors · 6 periods (1990–2015)
- 99.57% completeness on core variables
- 109 harmonised predictors in 12 thematic groups
Source table
| # | Pillar | Source | Variables | Period | Coverage | License |
|---|---|---|---|---|---|---|
| 1 | Migration Flows | Abel & Cohen (2019) | Bilateral flow estimates | 1990–2015 | 230 countries | CC BY 4.0 |
| 2 | Migration Flows | UN DESA IMS 2024 | Bilateral stock matrices, diaspora calibration | 1990–2020 | 232 countries | UN ToU |
| 3 | Climate | CRU TS 4.09 (Harris et al. 2020) | tmp, pre, tmx, tmn, dtr, vap, wet, SPEI | 1901–2022 | 0.5° grid, global land | OGL v3 |
| 4 | Climate | CMIP6 ScenarioMIP (5 GCMs) | tas, pr → anomalies (4 RCPs) | 2015–2100 | Global | CMIP6 ToU |
| 5 | Climate | WMO (2017) | Baseline normals 1961–1990 | Reference | Global | WMO |
| 6 | Economic | World Bank WDI (2024) | gdp_pc_ppp, urban_pct, agr_va_pct, sec_enroll, poverty_rate | 1960–2024 | 217 countries | CC BY 4.0 |
| 7 | Governance | WGI (Kaufmann et al. 2011) | wgi_ge, wgi_rl | 1996–2023 | 215 countries | CC BY 4.0 |
| 8 | Conflict | UCDP v25.1 (Davies et al. 2023) | Battle-related deaths | 1989–2023 | Global | CC BY 4.0 |
| 9 | Vulnerability | ND-GAIN | Climate adaptation score (0–100) | 1995–2023 | 192 countries | CC BY-SA 4.0 |
| 10 | Disasters | EM-DAT (Guha-Sapir et al.) | Natural hazard events | 1900–present | Global | Academic |
| 11 | Education | Barro-Lee v3 (2013) | yrs_school_mean, sec_complete, tert_complete | 1950–2010 | 146 countries | Free |
| 12 | Demography | UN WPP 2024 | Population by age-sex | 1950–2100 | 237 countries | CC BY 3.0 IGO |
| 13 | Gravity | CEPII GeoDist (Mayer & Zignago) | log_distw, contig, comlang_off, colony | Static | ~40,000 dyads | Free |
| 14 | Policy | DEMIG VISA (de Haas et al.) | visa_required (0/1) | 2000–2015 | 214 countries bilateral | Academic |
| 15 | Socioeconomic | IIASA SSP Database v3.1 | Future GDP, population, education | 2020–2100 | 197 countries × 4 SSPs | CC |
| 16 | Displacement | NASA SEDAC LECZ v3 | Coastal population <5 m, <10 m | 1990, 2000, 2015 | 234 countries | CC BY 4.0 |
| 17 | Displacement | IPCC AR6 WG1 Table 9.9 | Sea level rise projections | 2020–2150 | Global, 4 SSPs | IPCC |
Notes on the panel
Panel: 316,020 directed dyad-period observations (230 countries × 52,670 dyads × 6 periods, 1990–2015). 99.57% completeness on core variables.
GCM ensemble (CMIP6): ACCESS-CM2, GFDL-ESM4, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-LR. Selection rationale: documented performance, independence across 5 research centers, availability across all 4 RCPs.
Anomaly baseline: WMO 1961–1990; projections calibrated via model-specific 1995–2014 reference.
SSP→RCP mapping: SSP1 = ssp126, SSP2 = ssp245, SSP3 = ssp370, SSP5 = ssp585.
Missing data: core predictors < 0.1% missing. Time-invariant bilateral variables (distance, colonial ties) need no imputation. Slowly evolving variables (GDP, governance) use linear interpolation.
License & access
The harmonised projections and indicator panel produced by the Migration Scenario Engine are released under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Raw third-party inputs remain under their respective upstream licenses (see the table above).
Free downloads (country-level scenario projections for all 5 scenarios, indicator panels, OOF predictions, SHAP feature attributions) are available from the interactive dashboard at migrationengine.org. Dyad-level data are available on request via rogalski.academic@pm.me.
How to cite
Rogalski, C. (2026). Migration Scenario Engine: Global Bilateral Migration Projections under Climate Scenarios, 2020–2100. CERIFR Research. https://migrationengine.org