Data: Five-Scenario Comparison (Country-Level)

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Article 6-3

Data: Five-Scenario Comparison (Country-Level)

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↓ Download the full CSV (192 countries, 31 columns)

This is the country-level comparison file behind the Part 6 analysis. For each country it gives the current manufacturing employment and the end-of-programme target (illustratively 2061) under all five redistribution scenarios — Equity-leaning, Balanced, Efficiency-leaning, Unemployment-Leveling, and Informal Insertion (the proposed implementation) — along with the change from baseline, the underlying demand signals, and the informal-to-formal transition figures.

All employment figures are in thousands of jobs. So China’s current total of roughly 215,000 in the file means about 215 million workers. Targets are end-of-programme outputs (illustratively 2061); deltas are the change from the baseline (illustratively 2036). The interactive table below shows a rounded view of the most-used columns; the downloadable CSV contains every column at full precision.

A note on the numbers. The downloadable CSV is the raw five-scenario model file covering 192 countries. The interactive table below shows 194 countries, and its Informal Insertion column comes from a later, cleaner run of the same model — the figures used throughout the Part 6 articles and in the Article 6-4 — Phased Implementation Data (for example, China’s end-of-programme Informal Insertion total reads ~186M here, versus ~182M in the raw five-scenario file). Why the difference: the model requires that global manufacturing stay constant — every job removed from one country must reappear in another (the zero-sum rule). The later run preserves that total exactly (523.5M in = 523.5M out) and covers all 194 countries; the earlier five-scenario run came up about 0.4M short of the global total and was missing two countries, Ethiopia and Venezuela. So the table shows the corrected figure, while the raw file remains downloadable so nothing is hidden. The two extra countries appear in the table with ”—” in their other-scenario cells, because the source lacked detailed scenario-level data for them. The four other scenarios (Balanced, Equity, Efficiency, Unemployment-Leveling) are taken from the five-scenario file in both the table and the download.

Explore the data

Sort any column by clicking its header, search by country name or ISO code, and filter by region, income group, or feasibility flag. Click anywhere on the table — or the button below — to open it full screen.

⤢ Open full screen

What the columns mean

The downloadable CSV has 31 columns. Here is what each one represents.

Column	Meaning
iso3	ISO 3166-1 alpha-3 country code.
un_name	UN member-state name.
wb_region	World Bank region grouping.
wb_income_group	World Bank income classification (Low / Lower-middle / Upper-middle / High income).
feasibility_flag	Implementation-context flag: stable, below_average, fragile, or severe_conflict.
current_total	Current (baseline) total manufacturing employment, in thousands.
target_balanced	End-of-programme target under the Balanced scenario (40% equity / 35% efficiency / 25% capability).
target_equity	End-of-programme target under the Equity-leaning scenario (60% equity weight).
target_efficiency	End-of-programme target under the Efficiency-leaning scenario (60% efficiency weight).
wap	Working-age population (used to weight redistribution shares), in thousands.
delta_balanced	Change from current to Balanced target (target_balanced − current_total).
delta_equity	Change from current to Equity target.
delta_efficiency	Change from current to Efficiency target.
pct_change_balanced	Balanced delta expressed as a percentage of current employment.
unemp_rate_filled	Official unemployment rate used as model input (gaps filled from peer data).
mfg_target_unemp_leveling	End-of-programme target under the Unemployment-Leveling scenario.
unemp_rate_2050_unemp_lev_pct	Projected 2050 unemployment rate under Unemployment-Leveling.
baseline_unemp_2050_K	Projected 2050 unemployed persons under baseline (no redistribution), in thousands.
labor_force_2050_K	Projected 2050 labor force, in thousands.
total_emp_2050_K	Projected 2050 total employment, in thousands.
delta_unemp_leveling	Change from current to Unemployment-Leveling target.
mfg_target_informal_insertion	End-of-programme target under Informal Insertion — the proposed implementation.
unemp_rate_2050_ii_pct	Projected 2050 unemployment rate under Informal Insertion.
mfg_employment_informal_thousands	Estimated informal manufacturing employment, in thousands.
informal_share	Informal share of manufacturing employment before redistribution (0–1).
informal_share_after	Informal share after Informal Insertion redistribution (0–1).
informal_formalized	Workers moved from informal to formal manufacturing, in thousands.
demand_unemp	Demand signal from official unemployment above the 10% ceiling (Path 1), in thousands.
demand_informal	Demand signal from 25% of informal manufacturing workers (Path 2), in thousands.
effective_demand	Effective demand = MAX(demand_unemp, demand_informal), in thousands.
delta_informal_insertion	Change from current to Informal Insertion target.

Coverage: 192 countries with direct or peer-proxy data. Full methodology, scenario definitions, and data provenance are in Article 6-2 — The Redistribution Methodology. Data sources include UN WPP 2024, ILO ILOSTAT, UNIDO INDSTAT, UN Comtrade, World Bank WGI/WDI/LPI, UNESCO UIS, USGS, and the Energy Institute — all consulted May 2026.

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