Data Connect Conference · Women in Analytics

She Displaced

Where climate hits women hardest.

Best experienced with audio. Press play and keep scrolling — what the dashboard shows, why it matters, and who the numbers are actually about.

159Countries Scored

20Years of Data

150MWomen at Risk*

Climate doesn't displace people equally.

When floods rise, droughts deepen, and extreme heat becomes unbearable — women in climate-vulnerable regions face compounded risks. Limited economic mobility, restricted access to resources, and political exclusion mean that the same disaster hits women harder.

Yet this intersection is rarely mapped transparently. That's what the She Displacement Risk Score (SDRS) does — openly, and across 20 years. See methodology →

The She Displacement Risk Score (SDRS)

"150 million women are one storm away from displacement. But the risk isn't just in the clouds — it's in the social fabric."

The SDRS combines climate vulnerability, adaptive capacity gaps, and gender inequality into a single score. Toggle between layers to see how adding gender changes the map.

Year: 2025

How Gender Amplifies Climate Risk

"Why does a moderate storm in Country A cause 10× more displacement than a major hurricane in Country B? Meet the Gender Penalty."

The same climate hazard hits harder where women have less power. The slope chart shows how countries jump in risk rank once gender inequality is factored in. The delta chart quantifies exactly how much of that risk is driven by gender alone — revealing the highest-leverage targets for policy intervention.

Year: 2025

Rank Shift The Gender Penalty Left = vulnerability rank · Right = SDRS rank after adding gender

Top 7 movers — gender amplifies risk most Other countries in top 20

Delta Score Gender Displacement Delta SDRS − Climate Vulnerability · How much gender adds to risk

Bar color intensifies with stronger gender-driven risk

◆ Iran's composite risk runs +0.286 above what pure climate exposure would predict — the largest social amplification among high-risk countries. These are the highest-leverage targets for gender-equity investment as climate adaptation.

Her Risk Fingerprint

"Every crisis has a unique DNA. Is it a lack of water security, or a lack of political voice?"

Every country and region carries a unique risk shape — the intersection of climate exposure, gender inequality, and adaptive capacity. Switch between the Regional Gallery for a macro scan across regions, or the Country Comparison to drill into any two countries side by side, with each country's profile shown against its regional average.

How to read Climate (food, water, health) Gender (economic, political, educational) Readiness (economic, governance, social) Each axis is a sub-dimension · Larger polygon = higher combined risk · axes fixed 0→1

Country A

Country B

Dashed outline = regional average for that country's region

✦ Positive Outlier

Risk is Mitigatable Through Policy

Rwanda has high climate vulnerability (0.567) — yet its SDRS is significantly lower because gender equality (0.790) and improving readiness partially offset the hazard. This proves the risk is not inevitable. Gender equity is a climate adaptation strategy.

Climate Vulnerability

0.567

Top 6% globally

→

SDRS Score

0.487

Pulled down by gender equity

Gender Gap Score

0.790

Political Empowerment

0.560

Climate Vulnerability

0.567

Algeria's gender penalty is nearly double Rwanda's — closing that gap is the fastest, cheapest climate-adaptation investment available, because it reduces risk without requiring large-scale infrastructure change.

Climate Crime Scene

"Disasters are the spark. Inequality is the fuel."

Every disaster leaves evidence. Select a country to read its climate fingerprint — what types of hazards dominate, how lethal they are relative to gender inequality, and how the river of displacement has flowed across two decades. Spot the Silent Crises — years with low deaths but massive, life-altering female displacement.

Genetic Makeup What Threatens Her Each of the 100 cells represents 1% of the affected population. The pattern is this country's climate DNA.

Catastrophe Matrix How Bad It Gets Each dot is one year · Axes use log scale · Quadrants split at this country's median event. Where deaths are low but millions are displaced — that's the silent crisis.

River of Risk Two Decades of Displacement — 2006 to 2025 Stream thickness = people affected. Hover the white pulses to reveal Gender Resilience Checkpoints at each peak.

Compounding Risk & Country Trajectories

"Risk is not static. Some nations are racing toward a breaking point, while others are decoupling risk from vulnerability."

Click any country in the scatter plot to drill into its 20-year journey. The line chart decomposes why its risk changed — was it the climate, or the gender gap? Color shows risk velocity: how fast each country is moving toward or away from crisis.

YEAR 2006

2006 2025

BUBBLE SIZE Affected population in the selected year

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Regional Risk Trajectories

Mean SDRS by region · click a bubble for country detail

Regional mean SDRS Global Median

SDRS Score Gender Penalty Climate Vulnerability Global Median

The Innovation Lab

"The future is adjustable. What happens if we prioritize gender equality over physical readiness?"

Drag the sliders to reweight the four SDRS components and watch the top-10 leaderboard rearrange itself. Every ranking is a reflection of the priorities behind it — change the priorities, change who we call most at risk. SDRS is a lens, not a verdict.

Weight Selector = 1.00

Drag any slider — the others adjust proportionally to keep the total at exactly 1.0.

Climate Vulnerability 0.30

Adaptive Capacity Gap 0.20

Gender Penalty 0.35

Disaster Burden 0.15

Presets

Gender-forward · The thesis of this index

Country Spotlight

Search a country to track its rank as you adjust weights.

Top 10 by SDRS — Live Ranking 2025

Ranks update instantly as you drag the weights. Bars show why each country ranks — segmented by the 4 SDRS components. Higher score = higher displacement risk.

Climate Adaptive gap Gender Disaster

The Path Forward

"Policy is protection. These nations show that closing the gender gap is a powerful — and measurable — climate defense."

Not all trajectories point toward crisis. These are the Resilience Champions — countries that have made the greatest progress in reducing their SDRS since their peak risk year. Their stories are the blueprint.

She Displaced.

A risk that's measurable. And preventable.

159

Countries Scored

Closing the Gender Gap

~7yrs

Peak → Recovery†

Still in Compounding Crisis†

Built for Data Connect Conference 2026 · Women in Analytics

Technical Documentation

Methodology

How the She Displacement Risk Score is constructed, why each component was chosen, and what the data means for policy.

The SDRS Formula

The She Displacement Risk Score (SDRS) is a weighted composite of four dimensions, each normalized to [0,1] relative to the global cohort for that year. Higher = more pressure toward women-specific climate displacement.

SDRS = 0.30 × n(Climate Vulnerability)

+ 0.20 × n(1 − Readiness)

+ 0.35 × n(1 − Gender Gap Score)

+ 0.15 × n(Disaster Burden)

Why these weights?

Gender inequality receives the largest weight (35%) — without it, SDRS would just be a tweaked ND-GAIN index. Climate vulnerability (30%) is the best-established signal in the literature. Readiness (20%) gets less weight because it partially overlaps with vulnerability in ND-GAIN's own construction. Disaster burden (15%) is intentionally smallest — one cyclone shouldn't spike a country to #1 without a real structural change.

Within-year normalization

Each component is min-max normalized across all countries within the same year before weighting. This means SDRS measures relative risk among peers that year — a country improving while others worsen still shows as high risk. Scores are comparable across years even when ND-GAIN or WEF shift their baselines.

Disaster burden compression

EM-DAT has extreme outliers (Pakistan 2022: 33 million affected). Raw values would let one event dominate. The log-compressed formula is:
D = 0.5×log(1+Affected) + 0.3×log(1+Deaths) + 0.2×log(1+Events)

Climate Vulnerability

Source: ND-GAIN Country Index

The vulnerability score from the Notre Dame Global Adaptation Initiative (ND-GAIN) measures a country's exposure, sensitivity, and adaptive capacity to climate change across six life-supporting sectors.

Foodvulnerability_foodProjected change in cereal yields; food import dependency

Watervulnerability_waterFreshwater availability; flood risk; groundwater stress

Healthvulnerability_healthDisease burden; medical staff per capita; access to clean water

Ecosystem Servicesvulnerability_ecosystemsBiodiversity; protected land area; ecosystem dependency

Habitatvulnerability_habitatUrban concentration; slum population; sea-level exposure

Exposurevulnerability_exposurePopulation exposed to extreme weather events

Why it's used as-is: The ND-GAIN vulnerability score is already a composite of these six sectors, normalized 0–1. Using it directly preserves the methodological integrity of a peer-reviewed index while keeping SDRS interpretable.

Adaptive Capacity Gap

Source: ND-GAIN Readiness Index

Readiness measures a country's ability to leverage investments and improvements for adaptation. It covers three sectors:

Economic Readinessreadiness_ecoBusiness environment; trade freedom; regulatory quality

Governance Readinessreadiness_govPolitical stability; rule of law; control of corruption

Social Readinessreadiness_socialEducation; ICT infrastructure; innovation capacity

Adaptive Capacity Gap = 1 − Readiness

Why inverted: A high readiness score means a country can adapt — so it reduces displacement risk. Inverting it converts readiness into a "gap" metric where higher = more vulnerable, consistent with the other components.

Gender Penalty

Source: WEF Global Gender Gap Report

The WEF Gender Gap Index measures parity between women and men across four sub-indices. A score of 1.0 = full parity; 0.0 = complete disparity.

Economic Participationecon_participationLabor force participation; wage equality; senior roles

Political Empowermentpolitical_empowermentWomen in parliament; ministerial positions; head of state

Educational Attainmenteducational_attainmentLiteracy rate; enrollment in primary, secondary, tertiary

Health & Survivalhealth_survivalSex ratio at birth; healthy life expectancy gap

Gender Penalty = 1 − Gender Gap Score

Why inverted: A high gender gap score means women are more equal — which reduces displacement risk. Inverting it creates a "penalty" metric: how much gender inequality is adding to risk. As of 2025, Afghanistan carries one of the world's highest gender penalties (0.418) — the largest single-year value reachable under the WEF Gender Gap Index methodology, and a structural disadvantage that compounds every climate event.

Gender Displacement Delta (GDD)

GDD = SDRS − n(Climate Vulnerability)

The GDD compares each country's composite SDRS against a "climate-only" counterfactual — what the index would say if physical climate exposure were the only driver. The sign tells the story: positive means gender, adaptive capacity, and disaster burden are pushing displacement risk above what climate exposure alone predicts; negative means climate is the dominant factor and social dimensions are not compounding it.

Policy interpretation: A country with GDD > +0.15 has its displacement risk substantially driven by gender inequality or weak adaptive capacity — the highest-leverage targets for social intervention. A country with GDD < 0 faces a risk profile dominated by its physical climate exposure, where mitigation and adaptation infrastructure matter most.

Iran

GDD = +0.286

Composite risk is well above pure climate exposure — gender inequality and adaptive gaps are the primary drivers

Pakistan

GDD = +0.164

Extreme climate exposure plus elevated social risk — both dimensions compound into a true crisis

Rwanda

GDD = −0.327

High climate exposure, but gender progress and readiness investment are measurably reducing composite risk below the climate-only baseline

Data Sources & Coverage

ND-GAIN Country Index ↗

Notre Dame Global Adaptation Initiative

Annual vulnerability and readiness scores for 182 countries, 1995–2023. For 2024–2025, the Last Observation Carried Forward (LOCF) approach was applied — 2023 values are held constant. Peer-reviewed methodology combining 45 indicators across 6 life sectors.

Columns used: vulnerability, readiness, and all sub-dimensions

Global Gender Gap Report ↗

World Economic Forum

Annual gender parity index for 146+ countries, 2006–2025. Covers economic, political, educational, and health dimensions. Data available through 2025.

Columns used: Overall score, economic participation, political empowerment, educational attainment, health & survival

EM-DAT International Disaster Database ↗

Centre for Research on the Epidemiology of Disasters

Global database of natural and technological disasters since 1900. Climate disaster data available through 2025. Used for total deaths, total affected, and disaster count per country-year.

Columns used: Total Deaths, Total Affected, Disaster count

Data Vintage & Forward-Filling (2024–2025)

The SDRS spans 20 years (2006–2025). Not all source datasets publish at the same cadence:

Climate Vulnerability (ND-GAIN)2023LOCF — 2023 values carried forward

Adaptive Capacity / Readiness (ND-GAIN)2023LOCF — 2023 values carried forward

Gender Gap Index (WEF)2025Observed — no imputation needed

Disaster Burden (EM-DAT)2025Observed — no imputation needed

LOCF (Last Observation Carried Forward) assumes that a country's climate vulnerability and readiness did not materially change between 2023 and 2025. This is a conservative assumption — structural indicators like infrastructure, governance, and ecosystem sensitivity shift slowly. The Gender Gap and Disaster Burden components use observed 2025 data, ensuring the SDRS still captures recent changes in gender policy and acute climate events.

Country Coverage

The dataset contains 187 country codes across all sources. Of these, 159 countries have sufficient data across all four SDRS components (ND-GAIN vulnerability, ND-GAIN readiness, WEF Gender Gap Index, EM-DAT disasters) to compute a complete SDRS score. The remaining 28 countries have partial coverage (typically missing Gender Gap Index data for small island states or microstates) and appear in component-specific charts but are excluded from the composite SDRS. The "159 countries scored" figure used throughout refers to countries with full SDRS coverage.

"High Risk" Threshold Definition

Throughout the dashboard, "high risk" refers to the top 20% (quintile) of the 2025 SDRS distribution — the 29 countries with the highest composite scores among the 146 scored in 2025 (SDRS ≈ 0.597 and above). This compounding-crisis set is where climate vulnerability and gender inequality are structurally deepest, and both drive risk simultaneously. The quintile boundary separates countries where SDRS is elevated by multi-component stressors from those where a single dimension dominates.

Limitations & Caveats

Weight sensitivity: The 30/20/35/15 weights are theoretically motivated but not empirically derived. The Innovation Lab (Chapter 6) allows users to test alternative weightings and observe how rankings shift.
Gender Gap Index coverage: The WEF index is only available from 2006, limiting the full SDRS to 2006–2025 (20 years). Vulnerability and readiness data extends back to 1995.
Forward-filling for ND-GAIN: Climate vulnerability and readiness scores for 2024–2025 use Last Observation Carried Forward (LOCF) from 2023. Structural indicators shift slowly, but this means the SDRS may understate rapid environmental degradation in those years.
Country-level aggregation: All scores are national averages. Sub-national variation — which is often where the most vulnerable women live — is not captured.
Correlation between components: Climate vulnerability and gender inequality are not independent — poorer countries tend to score worse on both. The SDRS does not attempt to decompose this correlation.
Displacement as outcome: The SDRS predicts risk of displacement, not actual displacement. Actual displacement data is fragmented and inconsistently reported globally.