Methodology — Sovereign Economic Resilience Index (SERI)

Blomstra Insights/Methodology Standards/Sovereign Economic Resilience Index

Methodology · Strategic Intelligence

Sovereign Economic Resilience Index

How SERI measures a sovereign economy’s capacity to absorb, adapt, and recover from structural shocks.

Version 4.2.1 · Standard: BMS‑1.0.0 · Last reviewed: 2026-08-11

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Fiscal Space

External Vulnerability

Economic Sophistication

Institutional Resilience

What SERI measures

SERI assesses the structural resilience of a sovereign economy — its ability to withstand shocks, adapt to changing conditions, and recover without fundamental collapse. Unlike a growth forecast or a credit rating, SERI focuses on deep structural characteristics that change slowly: fiscal headroom, external balance sheets, economic complexity, and institutional depth. A higher composite score means greater resilience — countries are ranked with the highest score as #1, so #1 is the most resilient economy, not the largest or richest.

The four pillars

Fiscal Space

25%

Government’s capacity to respond to shocks without destabilising public finances. Measured through debt-to-GDP, deficit trajectory, interest burden, and revenue base depth.

fiscal_score = f(debt/GDP, deficit/GDP, interest/revenue, tax/GDP)

Source — IMF WEO, World Bank Direction — lower debt, lower deficit = higher resilience

External Vulnerability

25%

Exposure to external imbalances and capital-flow reversals. Captures current-account position, foreign-exchange reserves, external debt composition, and export concentration.

external_score = g(reserves/imports, current_account/GDP, short‑term_debt/reserves)

Source — IMF WEO, World Bank, UN Comtrade Direction — higher reserves, lower external debt = higher resilience

Economic Sophistication

25%

The complexity and diversity of a country’s productive structure — more sophisticated, diversified economies are better at absorbing sectoral shocks and adapting to new conditions.

sophistication_score = h(ECI, product_diversity, services_share)

Source — Harvard Atlas of Economic Complexity, WDI Direction — higher ECI, more diversification = higher resilience

Institutional Resilience

25%

Quality of governance, rule of law, and institutional capacity to manage crises effectively and credibly.

institutional_score = k(governance, rule_of_law, corruption_control, regulatory_quality)

Source — WGI, V-Dem, Transparency International Direction — higher governance quality = higher resilience

Composite score

Each pillar is percentile-ranked against all countries with data for it, then combined by weight — the shared normalization and weighting rules are covered in full on the Methodology Standards page. For SERI specifically:

composite = (fiscal_pct × 0.25) + (external_pct × 0.25) + (sophistication_pct × 0.25) + (institutional_pct × 0.25)

Coverage & partial ranks

SERI requires at least 3 of its 4 pillars to publish a rank — the full Full/Partial/Insufficient system and the injection algorithm behind partial ranks are covered on the Methodology Standards page. In practice for SERI:

4 / 4FullDefinitive rank — FULL

3 / 4PartialRank shown as a range, e.g. #38–#52* — PARTIAL

< 3 / 4InsufficientExcluded from the published index

Data quality output

Every country’s published record carries a per-pillar quality summary — coverage, staleness, and the good/aged/stale mix behind it — following the provenance standard above. Full schema details are in the developer documentation, not published here.

Data sources

Pillar	Primary source	Refresh cadence	Coverage
Fiscal Space	IMF WEO, World Bank	Weekly (cached), quarterly (live)	~196 countries
External Vulnerability	IMF WEO, World Bank, UN Comtrade	Weekly	~196 countries
Economic Sophistication	Harvard Atlas of Economic Complexity, WDI	Weekly	~180 countries
Institutional Resilience	WGI, V-Dem, Transparency International	Weekly	~190 countries

Deviations from BMS‑1.0.0

SERI is BMS‑1.0.0 conformant with one documented deviation:

Economic Sophistication — multi‑source composite

BMS‑003 assumes one series per indicator. Economic Sophistication is a derived composite of ECI, product diversity, and services share — combined before percentile ranking. Documented here per BMS‑003 rule 6.

Version history

v4.2.1Current. Refined pillar weights and added data-quality output per BMS‑001. Normalisation migrated to BMS‑003 percentile ranking.

v3.0.0Renamed from “Economic Resilience Index” to “Sovereign Economic Resilience Index (SERI)”. Expanded from 3 to 4 pillars. Ranking direction inverted so #1 is most resilient.

In short: SERI combines four structural pillars — fiscal space, external vulnerability, economic sophistication, and institutional resilience — into one percentile-based composite. It is explicit when a country’s rank depends on incomplete data and publishes the source and age of every number behind the score. Full mechanics: Methodology Standards, BMS‑1.0.0.

Part of the Sovereign Risk family

Methodology — Sovereign Infrastructure Vulnerability Index

Blomstra Insights/Methodology Standards/Sovereign Infrastructure Vulnerability Index

Methodology · Strategic Intelligence

Sovereign Infrastructure Vulnerability Index

How SIVI measures a country’s structural exposure to infrastructure disruption — and how to read its ranks.

Version 2.0.0 · Standard: BMS‑1.0.0 · Last reviewed: 2026-08-11

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Energy Dependency

Maritime Exposure

Single‑Supplier Concentration

What SIVI measures

SIVI scores every country on how structurally exposed it is to disruption in the systems it depends on to function — where its energy comes from, how reliant it is on maritime shipping lanes, and how concentrated its trading relationships are with any single partner. It is not a measure of infrastructure quality or investment — a country can have excellent infrastructure and still score as highly exposed if that infrastructure depends on a small number of external sources. A higher composite score means higher vulnerability — countries are ranked with the highest score as #1, so #1 is the most exposed country in the index, not the least.

The three pillars

Energy Dependency

33.33%

Consumption-weighted average of import dependency across five fuel types — coal, natural gas, petroleum, nuclear, renewables — so a country’s mix of energy sources determines how much weight each fuel’s dependency carries.

dependency = Σ (fuel_consumption / total_consumption) × (1 − fuel_production / fuel_consumption) × 100

Source — U.S. EIA International Energy Data If missing — pillar excluded from composite for that country

Maritime Exposure

33.34%

Inverted Liner Shipping Connectivity Index — fewer, less‑frequent shipping connections score as more exposed. Landlocked states receive a structural zero, the reference case in the shared standard’s own Structural Zero Rules — not a gap, and not a deviation.

exposure = 100 − percentile_rank(LSCI)

Source — World Bank WDI (IS.SHP.GCNW.XQ) Structural zero — 44 landlocked states, UN‑OHRLLS list If missing — pillar excluded from composite for that country

Single‑Supplier Concentration

33.33%

Herfindahl–Hirschman Index of import‑partner concentration — how much of a country’s total imports come from its largest trading partners, versus being spread across many.

HHI = Σ (import_share_i)² × 10,000

Source — UN Comtrade, total imports by partner Scale — 0 (fully diversified) to 10,000 (single source) If missing — pillar excluded from composite for that country

Composite score

composite = (energy_pct × 0.3333) + (hhi_pct × 0.3333) + (maritime_pct × 0.3334)

Coverage & partial ranks

SIVI requires at least 2 of its 3 pillars to publish a rank — the full Full/Partial/Insufficient system and the injection algorithm behind partial ranks are covered on the Methodology Standards page. In practice for SIVI:

3 / 3FullDefinitive rank — FULL

2 / 3PartialRank shown as a range, e.g. #38–#52* — PARTIAL

< 2 / 3InsufficientExcluded from the published index

Data quality output

Data sources

Pillar	Primary source	Refresh cadence	Coverage
Energy Dependency	U.S. Energy Information Administration (EIA)	Daily (cached), weekly (live)	~217 countries
Maritime Exposure	World Bank WDI — Liner Shipping Connectivity Index	Weekly	Coastal states + 44 structural‑zero landlocked states
Single‑Supplier Concentration	UN Comtrade — total import partner data	Weekly	Countries with an active Comtrade reporter code

Deviations from BMS‑1.0.0

SIVI is BMS‑1.0.0 conformant with two documented deviations:

Energy pillar — derived composite, not a single series

BMS‑003 assumes one series per indicator. Energy Dependency is instead a consumption-weighted composite of five separate EIA fuel series, combined before percentile ranking. Documented here per BMS‑003 rule 6.

HHI pillar — extended staleness window

BMS‑005 guidance is a 2-year staleness threshold for annual data. Trade-data reporting lags mean many valid reporters would be excluded at 2 years, so this pillar accepts a wider window, always preferring the most recent year available.

Not a deviation: the landlocked-country structural zero on the Maritime pillar is the reference example in BMS‑002’s own Structural Zero Rules, not a departure from it.

Version history

v2.0.0Current. Adopted percentile-rank normalization and the shared partial-coverage algorithm (BMS‑002) in place of raw composite scoring. Added per-indicator provenance and data-quality output (BMS‑001).

v1.0.0Renamed from Critical Infrastructure Vulnerability Index (CII) to Sovereign Infrastructure Vulnerability Index (SIVI). Ranking direction: highest composite score = #1 (most vulnerable). Methodology otherwise unchanged.

In short: SIVI combines three independently-sourced pillars into one percentile-based composite, is explicit when a country’s rank depends on incomplete data, and publishes the source and age of every number behind the score. Full mechanics: Methodology Standards, BMS‑1.0.0.

Part of the Sovereign Risk family

Governance Capture Risk Index — Methodology

Methodology

Governance Capture Risk Index

Estimating institutional vulnerability to capture by concentrated interests

Version 1.0 · Last updated July 2026

The Governance Capture Risk Index estimates the degree to which a country’s institutions — its regulatory system, its political process, its information environment, and its formal safeguards against undue influence — are vulnerable to capture by concentrated economic or political interests.

Higher score = higher risk Lower score = stronger resistance

This is a risk index built entirely from real, independently published data. No figures are estimated, interpolated, or fabricated by Blomstra. Every score traces back to a named public institution’s own published statistics.

The Four Pillars

Each pillar captures a distinct vector of capture risk. Together they answer a question no single metric can: not just how democratic a country is, but how robust its institutions are against private capture.

⚖️

Regulatory Capture

25% weight

Whether regulation serves the public interest or narrow private interests; state control over corruption.

🏛️

Political Capture

25% weight

Bribery and private influence in the executive, legislature, judiciary, and bureaucracy.

📰

Information Environment

25% weight

Freedom of expression, access to diverse information, and media integrity.

🛡️

Public Integrity Safeguards

25% weight

Lobbying transparency, revolving-door restrictions, whistleblower protection, and risk management.

⚙️ Adaptive coverage: If a country is missing one pillar, the remaining three are reweighted so their weights still sum to 100% — the missing pillar’s absence does not silently count against the country as a zero. If two or more pillars are missing, the country is excluded entirely, flagged as having insufficient data.

Data Sources

All data is sourced from internationally recognised institutions with open APIs and transparent methodologies — the same institutional standard used across every Blomstra index.

Pillar	Source	Indicator(s)	Coverage
Regulatory Capture	World Bank WGI	`GOV_WGI_RQ.SC` (Regulatory Quality), `GOV_WGI_CC.SC` (Control of Corruption)	~190 countries
Political Capture	V-Dem / Our World in Data	`political-corruption-index`	~180 countries
Information Environment	V-Dem / Our World in Data	`freedom-of-expression-index` + Media Corruption Score (averaged)	~180 countries
Public Integrity Safeguards	OECD	9 sub-measures across 6 topics (lobbying, revolving door, consultation, evaluation, whistleblower protection, risk management)	~40 countries (OECD members + partners)

🔍 Auditability: Every data point traces back to a verifiable public source. We do not use proprietary panels, paid consumer datasets, or black-box estimates.

How the Score Is Built

Step 1: Pillar-level risk scores

Each pillar produces a 0–100 risk score, oriented so that higher always means higher capture risk. This is done by inverting “good” indicators (where higher = better governance) and rescaling all sources to a common 0–100 axis.

Pillar Risk Score = f(raw data) → 0–100 where higher = more capture risk

Step 2: Composite aggregation

The four pillar risk scores are combined using a weighted average. The default weight is 25% per pillar, editable via the admin interface.

Composite Score = Σ(pillar_risk × pillar_weight) ÷ Σ(weights actually used)

Step 3: Missing-pillar handling

Missing exactly one pillar: The remaining three weights are renormalized to sum to 100%. The country is still scored, with its coverage count displayed.
Missing two or more pillars: The country is excluded entirely, flagged as INSUFFICIENT_DATA.
Why this matters: Since Pillar 4 (OECD Safeguards) covers only ~40 countries, most scores are built on three pillars. This is displayed transparently rather than concealed.

📊 Example: A non-OECD country with data for Pillars 1, 2, and 3 but no Pillar 4 would receive a composite score based on reweighted weights of 33.3% each for the three available pillars. The country is still ranked, but with a clear “3 of 4 pillars” coverage flag.

Coverage & Transparency

Not every country has equal data availability. World Bank and V-Dem data cover the large majority of the world’s countries. OECD’s Public Integrity Indicators, currently the newest and narrowest of the four sources, cover OECD member states plus a small number of formal adherents and key partners.

Every published score is shown alongside how many of the four pillars — and, for the Safeguards pillar specifically, how many of its underlying topics — actually contributed to it. Coverage is never hidden.

What Is Deliberately Not Included

A fifth pillar, covering tax and offshore secrecy enablement, is under active evaluation. It is not yet included because we have not identified a data source for it that meets our standard: real, institutionally published, and retrievable without manual downloads or ad hoc data handling.

We would rather publish four verified pillars than five, with one resting on a compromise.

Update Cadence

The index is rebuilt on a weekly automated schedule, drawing fresh data directly from each source’s own published data service. No manual intervention is required — the pipeline runs unattended, exactly like the Prosperity Index.

📅 190+ countries scored · 4 pillars · updated weekly

Limitations

The index measures institutional/proxy risk, not direct corporate influence in dollar or transactional terms.
Pillar 4 covers only ~40 countries, meaning most scores rest on three pillars — this is disclosed but remains a comparability consideration.
Some underlying measures are de jure (formal legal frameworks) rather than de facto (actual implementation), which can penalize countries with strong uncodified norms.
The Tax/Offshore dimension is entirely absent pending a resolvable data-access path.

Prosperity Index — Methodology

Methodology

Prosperity Index

A quantitative framework for measuring where people can actually thrive

Version 1.0 · Last updated 21 July 2026

The Blomstra Prosperity Index is a composite score (0–100) that quantifies a country’s real-world livability and opportunity. It combines five pillars — Income Potential, Affordability, Human Development, Freedom & Governance, and Safety — into a single, transparent measure designed for relocation research, investment context, and cross-country comparison.

A higher score indicates greater prosperity. The index is refreshed weekly and draws exclusively from authoritative, public data sources — never fabricated, never scraped, never manually estimated.

The Five Pillars

Each pillar captures a distinct dimension of what makes a country livable. Together they answer a question no single metric can: not just how rich a country is, but how well an individual can actually build a life there.

💰

Income Potential

20% weight

Real earning power, adjusted for how income is actually measured across economies.

💵

Affordability

20% weight

How far that income actually stretches against local prices.

❤️

Human Development

25% weight

Education access, connectivity, and life expectancy combined.

⚖️

Freedom & Governance

15% weight

Institutional quality, civil liberties, and rule of law.

🛡️

Safety

20% weight

Personal and institutional security, combined.

⚙️ Adaptive weighting: if a data point is genuinely unavailable for a country in a given week, that pillar is left out of its composite for that country rather than assumed — and the remaining weights are automatically rebalanced so the score always reflects real data, never a placeholder.

Data Sources

All data is sourced from internationally recognised institutions with open APIs and transparent methodologies — the same institutional standard used across every Blomstra index.

Pillar	Source	Indicator	Update Frequency
Income Potential	World Bank	`NY.GNP.PCAP.CD`	Annual (latest year available)
Affordability	World Bank / International Comparison Program	`PA.NUS.GDP.PLI`	Annual, refreshed weekly
Human Development	World Bank + World Health Organization	Education, connectivity & life expectancy indicators	Annual, refreshed weekly
Freedom & Governance	World Bank Worldwide Governance Indicators	Voice & Accountability, Rule of Law	Annual, refreshed weekly
Safety	World Bank	Political Stability, homicide rate	Annual, refreshed weekly

🔍 Auditability: Every data point traces back to a verifiable public source. We do not use proprietary panels, paid consumer datasets, or black-box estimates.

How the Score Is Built

Raw indicators arrive on wildly different scales — a homicide rate and a governance score have nothing in common numerically. Each pillar is independently normalized to a common 0–100 scale before being combined, using a calibration approach suited to that pillar’s real-world distribution (some are scaled logarithmically, where the difference at the low end matters far more than at the high end; others are scaled linearly, where differences are more evenly meaningful throughout the range).

Prosperity Score = Σ (pillar score × pillar weight) ÷ Σ (weight actually used)

The exact calibration constants for each pillar are part of Blomstra’s internal methodology and are not published in full, to preserve the integrity of the ranking against gaming or reverse-engineering — consistent with how comparable institutional indices (sovereign credit ratings, macro risk scores) handle their internal weighting logic.

Prosperity Tiers

Scores are mapped to five intuitive tiers:

🟢 Excellent

80 – 100

🟩 Very Good

65 – 79

🟡 Good

50 – 64

🟠 Fair

35 – 49

🔴 Needs Improvement

0 – 34

Strengths & Weaknesses Profiles

Beyond a single score, every country is profiled against its own five pillars to surface where it genuinely stands out and where it lags — relative to itself, not to an absolute bar. A country’s listed “weakness” can still be a strong number in absolute terms; the profile describes the shape of that country’s own balance, not a verdict on any single pillar.

Transparency & Data Vintage

Every refresh of the index reports the countries covered and the total scored — so you always know the scope of the current ranking. Underlying source data typically reflects the most recently reported year available per country, which can vary slightly between countries depending on how quickly each government or institution publishes.

📅 Example: 195 countries scored · updated weekly · sourced from World Bank and WHO

Regional and income-group aggregates (e.g. “Sub-Saharan Africa,” “High income”) are never included as rankable entities — only real countries are scored.

Limitations

The index is a snapshot based on the latest available official statistics — it does not predict future trajectory.
Some smaller or less-surveyed countries have thinner underlying data than major economies, which the index accounts for but cannot fully eliminate.
The index does not capture every dimension of lived experience (climate, culture, personal fit) — it is a quantitative starting point, not a substitute for on-the-ground research.
It is a comparative tool intended to inform decisions, not make them for you.

Geopolitical Risk Index — Methodology

Methodology

Geopolitical Risk Index

A quantitative framework for measuring sovereign security risk

Version 2.1.0 · Last updated 27 July 2026

The Blomstra Geopolitical Risk Index is a composite score (0–100) that quantifies country‑level geopolitical risk. It combines three pillars — Militarization, Conflict Intensity, and Displacement — into a single, transparent measure designed for sovereign risk assessment, portfolio allocation, and supply chain monitoring.

A higher score indicates higher geopolitical risk. The index is refreshed weekly and draws exclusively from authoritative, public data sources.

The Three Pillars

Each pillar captures a distinct dimension of geopolitical stress. They are weighted according to their direct impact on security and stability.

⚔️

Militarization

25% weight

Military expenditure as % of GDP. High spending signals external threat perception or internal repression.

💥

Conflict Intensity

45% weight

Battle‑related deaths per 100,000 population. The most direct measure of active violence.

🚶

Displacement

30% weight

Refugee outflows and inflows per 100,000. Outflow signals state collapse; inflow strains host states.

Data Sources

All data is sourced from internationally recognised institutions with open APIs and transparent methodologies.

Pillar	Source	Indicator	Update Frequency
Militarization	World Bank / SIPRI	`MS.MIL.XPND.GD.ZS`	Annual (latest year via MRV=1)
Conflict Intensity	UCDP (Uppsala Conflict Data Program)	GED (Georeferenced Event Dataset)	Annual, refreshed weekly
Displacement	UNHCR Refugee Data Finder	Refugee counts by origin & asylum	Annual, refreshed weekly

🔍 Auditability: Every data point is verifiable via the original source APIs. We do not use proprietary or black‑box data.

Why Logarithmic Scaling?

Raw indicators vary by orders of magnitude — a country might spend 0.1% of GDP on defence, while another spends 10%. A linear scale would let extreme values dominate the composite. We use logarithmic scaling to ensure that relative differences are captured fairly, and that no single outlier skews the index.

Score = ( log₁₀(Value) − log₁₀(Floor) ) / ( log₁₀(Ceiling) − log₁₀(Floor) ) × 100

Each pillar has a calibrated floor and ceiling that determine the 0–100 mapping. For conflict and displacement, we add 1 to the rate before taking the log to avoid zero values.

Composite Calculation

The final index is a weighted sum of the three pillar scores:

Risk = (Score_mil × 0.25) + (Score_conflict × 0.45) + (Score_displacement × 0.30)

Weights are fixed and reflect the relative importance of each dimension in driving overall geopolitical risk. If a pillar is unavailable for a given country (e.g., no conflict data), its weight is redistributed proportionally among the remaining pillars.

Risk Tiers

Scores are mapped to four intuitive risk categories:

🟢 Low

0 – 25

Stable, predictable

🟡 Medium

26 – 50

Elevated tensions

🔴 High

51 – 75

Active conflict, significant stress

🟣 Extreme

76 – 100

War, state collapse, mass atrocities

Transparency & Data Vintage

Every refresh of the index reports the exact data vintage for each pillar — so you know whether you are looking at 2024 or 2025 data. This is displayed in the admin panel and exposed via the REST API.

📅 Example: World Bank: latest available (mrnev=1) · UCDP: 2025 · UNHCR: 2024 · ACLED: trailing 365 days

No hidden assumptions. Every number is traceable to its source.

Limitations

The index is a snapshot — it does not predict future events.
Displacement data is a lagging indicator; by the time refugees are counted, the crisis has already occurred.
The index does not capture all dimensions of risk (e.g., cyber threats, trade dependencies, diplomatic isolation).
It is a quantitative tool intended to complement, not replace, qualitative analysis.
Data availability varies by country; some small states may have missing data for certain pillars, which may result in incomplete scores.