Worldwide susceptibility to systematic abuse: high-control groups cause serious harm worldwide, but no country reports prevalence and no institution tracks their human or financial cost. ISAJO’s High-Control Group Environmental Risk Index (HGERI) offers a first global estimate, by modelling the conditions that let systematic abuse in high-control groups thrive — weak legal protection, corruption, power imbalances, poor governance, and economic stress. While these is currently no direct data to assess systematic abuse in high-control groups, we use these available values as proxy metrics for a country’s susceptibility for such groups to operate with impunity, so that governments, researchers, journalists, families, and survivors can see where vulnerability is highest and why. This is only an attempt of what we want to do, and in the coming years we hope to create much better indirect and direct measurments.
Explore ISAJO’s High-Control Group Environmental Risk Index (HGERI). Use the dropdown to switch data layers. Green generally means safer, red means higher risk. “Overall risk (HGERI)” shows the composite banding (Very low → Very high) based on all components below.
Global HGERI Map
Why HGERI exists
High-control groups have caused profound harm across countries and generations, yet there is no shared data on where they operate or how much damage they cause. No country reports prevalence, no global institution tracks human or financial costs, and no international body monitors systematic psychological abuse. This data vacuum makes it harder for policymakers to act, researchers to study the problem, and ultimately families, partners and friends without support to regain contact with their loved ones. It can also make it so much harder to leave, when there is no pressure to keep the group from operating, or to support someone who wants to leave a high-control group.
HGERI works around that gap by measuring the risk environment rather than cult membership directly: legal protection against coercive control and domestic violence, governance quality, corruption, civil liberties, gendered power dynamics, socio-economic stress, and indicators of exploitation. It is a pragmatic first lens on a neglected issue, built from the best public datasets available, to inform governments wanting to strengthen protections for their citizens, researchers mapping coercion, journalists reporting hidden abuse, families seeking understanding, and survivors demanding accountability.
We also believe these factors play a part in how easily a new high-control group can establish itself, recruit, operate and keep hold of its members. Pressure from communities and goverments will very much shapre the systematic abuse, or lack thereof, in any new organisation established.
How HGERI is computed
HGERI identifies environments where high-control groups are more likely to thrive by integrating legal protections, governance quality, civil liberties, socio-economic stress, modern-slavery risk, digital freedom, and household/family power dynamics. Each source metric is normalised to a 0–1 risk scale (higher = more risk). Protective metrics (rule of law, corruption control, civil liberties, government response, internet freedom, WBL household protections) are inverted so weaker protection increases risk.
Legal protections and family-power structures (33%)
SAL1 (14%), DV1 (8%), WBL_CR1 (2%), WBL_OBEY1 (2%), WBL_HOH1 (1%), WBL_CIT1 (1%), SIGI_PI1 (5%), SIGI_FC1 (3%)
Governance and civic space (28%)
R1 (9%), R2 (9%), R3 (5%), D1 (5%)
Structural vulnerability & exploitation (39%)
S1 (10%), M1 (10%), M2 (8%), G1 (8%)
These components form HGERI_raw, which is then min–max scaled to HGERI (0–100) and classified into bands (Very low → Very high). With high meaning that this country has high susceptibility or risk for that high-control groups can operate with impunity. Missing inputs are down-weighted proportionally so that countries still receive a composite where sufficient data exists.
Data columns and why they matter
ISAJO-specific (unique inputs & outputs)
Systematic abuse law (level) (systematic_abuse_law_level) — ISAJO manual coding: 2=in force (France, Belgium), 1=draft/partial (UK), 0=none. Inclusion: legal recognition deters psychological domination and raises accountability.
Systematic abuse law risk (SAL1) — ISAJO risk transform (0–1; higher = more risk) used directly in HGERI weighting.
Overall risk (HGERI) (HGERI) — ISAJO composite 0–100 risk score from all components; HGERI_band maps to Very low → Very high.
HGERI raw (HGERI_raw) — ISAJO weighted composite before scaling to 0–100.
Other columns — HGERI_band (Very low → Very high) and country_name / iso3 identifiers (for joins and map geometry).
Source metrics (raw from datasets)
Prevalence per 1,000 (gsi_prevalence_per_1000) — Walk Free GSI 2023: estimated people in modern slavery per 1,000. Inclusion: entrenched exploitation increases baseline vulnerability.
Vulnerability score (gsi_vulnerability_score) — Walk Free GSI 2023 structural vulnerability (0–100). Inclusion: governance gaps, inequality, conflict make coercion easier.
Government response (gsi_gov_response_score) — Walk Free GSI 2023 state response (0–100; higher = stronger). Inclusion: weak response lowers deterrence for abusive groups.
Freedom House score (freedom_house_score) — Freedom in the World 2025 (0–100; higher = more free). Inclusion: restricted liberties hinder exit, whistleblowing, and support.
Rule of law (WGI) (wgi_rule_of_law) — World Bank WGI RL.EST (≈ -2.5 to 2.5; higher = stronger). Inclusion: weak courts reduce accountability and raise abuse risk.
Control of corruption (WGI) (wgi_control_corruption) — World Bank WGI CC.EST (≈ -2.5 to 2.5; higher = better control). Inclusion: corruption lowers the cost of operating abusive groups.
Youth unemployment rate (youth_unemployment_rate) — World Bank WDI youth (15–24) unemployment %. Inclusion: economic precarity and search for belonging increase recruitment risk.
Freedom on the Net (freedom_on_the_net) — Placeholder (all zeros) until Freedom House – Freedom on the Net is added. Inclusion: digital repression can block counter-speech and exit pathways.
Domestic violence law (WBL) (wbl_domestic_violence) — World Bank Women, Business and the Law 2024: strength of domestic violence legal protection. Inclusion: stronger protections raise accountability; weaker protections lower deterrence.
WBL – Choose residence (wbl_choose_residence) — WBL 2024: equal right to choose where to live (Yes/No). Inclusion: autonomy to leave/avoid coercive settings.
WBL – Obey husband (wbl_obey_husband) — WBL 2024: law free of obedience provisions (Yes/No). Inclusion: explicit obedience requirements enable coercive control.
WBL – Head of household (wbl_head_household) — WBL 2024: women can be head of household/family (Yes/No). Inclusion: household authority shapes ability to resist abuse.
WBL – Confer citizenship (wbl_confer_citizenship) — WBL 2024: equal rights to confer citizenship on spouses/children (Yes/No). Inclusion: legal dependence increases leverage for coercion.
SIGI – Physical integrity (sigi_physical_integrity) — OECD SIGI 2023: restricted physical integrity (higher = more discrimination). Inclusion: environments tolerating GBV/coercion raise risk.
SIGI – Discriminatory family code (sigi_family_code) — OECD SIGI 2023: discriminatory family law and practices. Inclusion: weak autonomy in family law lowers resistance to coercive control.
Derived risk components (built by ISAJO from sources)
DV1 – Domestic violence law (risk) (DV1) — Risk from WBL domestic violence protection (inverted). Weaker protection increases risk.
WBL residence (risk) (WBL_CR1) — Risk from unequal residence rights (inverted). Inclusion: constrained mobility increases coercion risk.
WBL obey husband (risk) (WBL_OBEY1) — Risk from obedience provisions (inverted). Inclusion: formal obedience raises systematic control risk.
WBL head of household (risk) (WBL_HOH1) — Risk from unequal head-of-household rights (inverted). Inclusion: lack of authority limits exit/protection.
WBL citizenship (risk) (WBL_CIT1) — Risk from unequal citizenship conferment (inverted). Inclusion: legal dependence heightens vulnerability.
R1 – Rule of law (risk) (R1) — Inverted/normalised WGI RL.EST; weaker rule of law increases risk.
R2 – Civil liberties (risk) (R2) — Inverted/normalised Freedom House score; less freedom increases risk.
R3 – Corruption (risk) (R3) — Inverted/normalised WGI CC.EST; more corruption increases risk.
S1 – Socio-economic (risk) (S1) — Normalised youth unemployment (WDI); higher unemployment increases risk.
M1 – Slavery prevalence (risk) (M1) — Normalised GSI prevalence; higher prevalence increases risk.
M2 – Vulnerability (risk) (M2) — Normalised GSI vulnerability; higher vulnerability increases risk.
G1 – Gov response (risk) (G1) — Inverted/normalised GSI response; weaker response increases risk.
D1 – Digital repression (risk) (D1) — Inverted/normalised freedom_on_the_net placeholder; less internet freedom increases risk.
SIGI PI (risk) (SIGI_PI1) — Risk from SIGI physical integrity discrimination (normalised). Higher discrimination increases risk.
SIGI family code (risk) (SIGI_FC1) — Risk from SIGI discriminatory family code (normalised). Higher discrimination increases risk.