Vulnerability Scoring Configuration#

This guide is for implementers setting up vulnerability assessment scoring for crisis response and emergency assistance programs. Vulnerability scoring identifies households at risk even if they're not currently poor.

What is Vulnerability Scoring?#

Vulnerability scoring assesses household risk and resilience factors:

  • Risk exposure - Natural disasters, conflict, health crises

  • Coping capacity - Assets, social networks, access to services

  • Adaptive capacity - Education, skills, economic diversity

  • Current shocks - Recent events affecting the household

Unlike PMT (which measures poverty), vulnerability scoring identifies households that could fall into poverty or crisis.

When to Use Vulnerability Scoring#

Use Vulnerability Scoring For

Don't Use For

Emergency cash transfers

Long-term poverty programs

Disaster response targeting

Universal basic income

Climate adaptation programs

Administrative census

Food security interventions

Routine service delivery

Epidemic response

Political targeting

Vulnerability vs. Poverty#

Aspect

PMT (Poverty)

Vulnerability Scoring

Measures

Current economic status

Risk of future crisis

Timeframe

Point-in-time snapshot

Forward-looking

Indicators

Assets, housing, income

Shocks, coping, resilience

Updates

Annual or biennial

After major events

Use Case

Routine social protection

Emergency response

You can use both: A household may be non-poor but highly vulnerable (e.g., small business owners in flood zones).

Vulnerability Components#

A comprehensive vulnerability model includes:

  1. Exposure Indicators - Geographic and demographic risk factors

  2. Sensitivity Indicators - Household characteristics that amplify shocks

  3. Adaptive Capacity Indicators - Resources to cope with shocks

  4. Current Shocks - Recent events impacting the household

Step-by-Step Configuration#

Step 1: Create the Vulnerability Model#

Go to Scoring → Scoring Models and create:

Field

Value

Name

2024 Vulnerability Assessment

Code

VULN_2024

Category

vulnerability

Calculation Method

weighted_sum

Active

☑ (after configuration)

Effective Date

2024-01-01

Description

Multi-dimensional vulnerability for disaster response

Step 2: Add Exposure Indicators#

Exposure indicators measure risk factors in the household's environment.

Geographic Hazard Risk#

Field

Value

Code

hazard_zone

Name

Hazard Zone Classification

Field Path

household.area_id.hazard_level

Weight

0.20

Calculation Type

mapped

Required

Yes

Sequence

1

Value Mappings:

Input Value

Output Score

Notes

high_risk

10

Flood plain, landslide area, cyclone path

moderate_risk

6

Secondary hazard zone

low_risk

3

Minimal environmental risk

no_risk

0

Safe zone

Distance to Services#

Field

Value

Code

distance_health

Name

Distance to Health Facility

Field Path

household.distance_to_health_km

Weight

0.08

Calculation Type

range

Required

Yes

Sequence

2

Value Mappings (Range):

Min

Max

Output Score

Notes

0

2

0

Within walking distance

2.01

5

3

Accessible with transport

5.01

10

6

Remote access

10.01

999

10

Very remote

Step 3: Add Sensitivity Indicators#

Sensitivity indicators amplify the impact of shocks.

Household Dependency#

Field

Value

Code

dependency_ratio

Name

Dependency Ratio

Field Path

household.dependency_ratio

Weight

0.15

Calculation Type

range

Required

Yes

Sequence

3

Formula for dependency ratio: (children under 15 + elderly over 64) ÷ (working age 15-64) × 100

Value Mappings (Range):

Min

Max

Output Score

Notes

0

50

0

Low dependency

50.01

100

5

Moderate dependency

100.01

200

8

High dependency

200.01

999

10

Very high dependency

How to create calculated dependency ratio:

Use a CEL formula indicator:

(household.members.filter(m, m.age < 15 || m.age > 64).size() /
 household.members.filter(m, m.age >= 15 && m.age <= 64).size()) * 100

Or create a variable in Studio → Variables and reference it.

Disability in Household#

Field

Value

Code

has_disability

Name

Household Has Member with Disability

Field Path

household.has_disabled_member

Weight

0.12

Calculation Type

mapped

Required

No

Default Value

False

Sequence

4

Value Mappings:

Input Value

Output Score

True

10

False

0

Female-Headed Household#

Field

Value

Code

female_headed

Name

Female-Headed Household

Field Path

household.head.gender

Weight

0.08

Calculation Type

mapped

Required

Yes

Sequence

5

Value Mappings:

Input Value

Output Score

Notes

female

8

Higher vulnerability in many contexts

male

0

Important: Adjust weight based on local context. In some settings, female-headed households may have equal or better outcomes.

Step 4: Add Adaptive Capacity Indicators#

Adaptive capacity reduces vulnerability through resources and skills.

Livelihood Diversity#

Field

Value

Code

income_sources

Name

Number of Income Sources

Field Path

household.income_source_count

Weight

0.12

Calculation Type

range

Required

Yes

Sequence

6

Value Mappings (Range - Inverse Scoring):

Min

Max

Output Score

Notes

3

99

0

Multiple income sources = less vulnerable

2

2

5

Two sources

1

1

10

Single income source = most vulnerable

0

0

10

No income

Savings or Assets#

Field

Value

Code

has_savings

Name

Has Savings or Productive Assets

Field Path

household.has_savings

Weight

0.10

Calculation Type

mapped

Required

No

Default Value

False

Sequence

7

Value Mappings:

Input Value

Output Score

True

0

False

10

Education of Household Head#

Field

Value

Code

head_education

Name

Education Level of Household Head

Field Path

household.head.education_level

Weight

0.08

Calculation Type

mapped

Required

Yes

Sequence

8

Value Mappings:

Input Value

Output Score

tertiary

0

secondary

3

primary

6

none

10

Social Support Network#

Field

Value

Code

social_network

Name

Access to Social Support

Field Path

household.social_support_score

Weight

0.07

Calculation Type

range

Required

No

Default Value

0

Sequence

9

Value Mappings (Range - Inverse):

Min

Max

Output Score

Notes

8

10

0

Strong network

5

7

4

Moderate network

2

4

7

Weak network

0

1

10

No support

How to assess: Use a simple survey question: "How many people/organizations can you ask for help in an emergency?" (0-10 scale)

Step 5: Add Current Shock Indicators#

Recent shocks increase immediate vulnerability.

Recent Job Loss#

Field

Value

Code

recent_job_loss

Name

Job Loss in Last 6 Months

Field Path

household.recent_job_loss

Weight

0.10

Calculation Type

mapped

Required

No

Default Value

False

Sequence

10

Value Mappings:

Input Value

Output Score

True

10

False

0

Recent Health Shock#

Field

Value

Code

health_shock

Name

Major Health Expense in Last Year

Field Path

household.recent_health_shock

Weight

0.10

Calculation Type

mapped

Required

No

Default Value

False

Sequence

11

Value Mappings:

Input Value

Output Score

True

10

False

0

Step 6: Define Vulnerability Thresholds#

Classify households by vulnerability level:

Min Score

Max Score

Classification Code

Classification Label

Priority

0.0

30.0

LOW_VULN

Low Vulnerability

4

30.01

50.0

MODERATE_VULN

Moderate Vulnerability

3

50.01

70.0

HIGH_VULN

High Vulnerability

2

70.01

100.0

EXTREME_VULN

Extreme Vulnerability

1

Priority: Use for targeting - "1" means "assist first" in emergency response.

Step 7: Test the Model#

Create test cases for different vulnerability profiles:

Test Case 1: Extreme Vulnerability#

Profile: Elderly female-headed household in flood zone, recently lost job

Indicator

Value

Score

Weight

Weighted

Hazard Zone

high_risk

10

0.20

2.0

Distance Health

8 km

6

0.08

0.48

Dependency Ratio

150%

8

0.15

1.2

Has Disability

True

10

0.12

1.2

Female Headed

female

8

0.08

0.64

Income Sources

1

10

0.12

1.2

Has Savings

False

10

0.10

1.0

Head Education

primary

6

0.08

0.48

Social Network

1

10

0.07

0.7

Recent Job Loss

True

10

0.10

1.0

Health Shock

True

10

0.10

1.0

Total

1.10

10.9

Final Score: 10.9 × (100/11) = 99.1 → EXTREME_VULN ✓

Test Case 2: Low Vulnerability#

Profile: Young dual-income household in safe area, educated, savings

Indicator

Value

Score

Weight

Weighted

Hazard Zone

no_risk

0

0.20

0

Distance Health

1 km

0

0.08

0

Dependency Ratio

40%

0

0.15

0

Has Disability

False

0

0.12

0

Female Headed

male

0

0.08

0

Income Sources

3

0

0.12

0

Has Savings

True

0

0.10

0

Head Education

tertiary

0

0.08

0

Social Network

9

0

0.07

0

Recent Job Loss

False

0

0.10

0

Health Shock

False

0

0.10

0

Total

1.10

0.0

Final Score: 0.0 → LOW_VULN ✓

Step 8: Activate and Deploy#

  1. Set Active = True on the model

  2. Train field teams on collecting vulnerability data

  3. Run initial assessment on target population

  4. Review distribution of classifications

  5. Adjust thresholds if needed based on available resources

Context-Specific Models#

Vulnerability indicators vary by context:

Climate/Disaster Vulnerability#

Key indicators:

  • Hazard zone classification

  • Housing structure quality

  • Access to early warning systems

  • Previous disaster experience

  • Evacuation plan awareness

Weights: High weight on geographic exposure (30-40%)

Food Security Vulnerability#

Key indicators:

  • Months of adequate food provisioning

  • Crop diversity (for farmers)

  • Access to markets

  • Food consumption score

  • Nutrition status of children

Weights: High weight on current food access (25-35%)

Health Crisis Vulnerability (e.g., Pandemic)#

Key indicators:

  • Household has elderly or immunocompromised

  • Access to healthcare

  • Ability to social distance (housing density)

  • Income disruption from lockdowns

  • Digital access for remote work/education

Weights: High weight on health risk factors (30-40%)

Conflict/Displacement Vulnerability#

Key indicators:

  • Displacement status (IDP, refugee, host community)

  • Duration of displacement

  • Legal status / documentation

  • Loss of assets during displacement

  • Social cohesion in host area

Weights: High weight on displacement factors (35-45%)

Dynamic Scoring#

Vulnerability changes faster than poverty. Consider:

Update Trigger

When to Re-Score

Natural disaster

Immediately after event

Economic crisis

Quarterly during crisis period

New shock data

When household reports new shock

Program exit

Before graduation/exit decisions

Routine

Every 6 months minimum

Best practice: Set up automatic re-scoring when key fields (like "recent_job_loss") are updated.

Integration with Programs#

Emergency Cash Transfer#

In eligibility manager:

Field

Value

Scoring Model

VULN_2024

Required Classifications

HIGH_VULN, EXTREME_VULN

Priority Order

Use score (highest first)

Layered Approach#

Combine vulnerability with poverty:

(pmt_score.classification in ['POOR', 'EXTREME_POOR']) &&
(vuln_score.classification in ['HIGH_VULN', 'EXTREME_VULN'])

This targets households that are BOTH poor AND vulnerable.

Benefit Tiers by Vulnerability#

Vulnerability

Benefit Package

EXTREME_VULN

Cash + food + shelter + psychosocial

HIGH_VULN

Cash + food

MODERATE_VULN

Food only

LOW_VULN

Monitoring (no immediate assistance)

Common Patterns#

Pattern 1: Rapid Assessment#

  • 5-8 indicators only

  • Focus on observable characteristics

  • Can be completed in 15-20 minutes

  • Lower precision but fast deployment

Use: Immediate post-disaster response

Pattern 2: Comprehensive Assessment#

  • 15-20 indicators

  • Include self-reported shock history

  • Requires 45-60 minute interview

  • Higher precision

Use: Planned vulnerability reduction programs

Pattern 3: Community-Led Assessment#

  • Simple indicator set (8-10)

  • Verified by community committees

  • Includes local knowledge factors

  • Weight community judgment

Use: Small-scale, community-based programs

Are You Stuck?#

Scores don't reflect reality in the field? Vulnerability is contextual. Review your indicators with local teams. What matters in one setting may not matter in another.

All households scoring high? You may be in a universally vulnerable population (e.g., refugee camp, disaster zone). Consider relative scoring or focus on specific sub-vulnerabilities.

Indicators changing too fast? Use shorter effective dates and plan for frequent re-scoring. Alternatively, focus on structural vulnerability (less volatile) rather than current shocks.

Need to combine with PMT? Create a composite score using CEL formula:

(pmt_score * 0.6) + (vuln_score * 0.4)

Difficult to collect some indicators? Mark non-critical indicators as not required and set reasonable defaults. It's better to have incomplete data than no vulnerability assessment.

Political pressure to adjust classifications? Document your methodology clearly. Use transparent, objective indicators. Make threshold decisions based on available budget and population needs, not individual cases.

Rapid Deployment Checklist#

When disaster strikes and you need to deploy quickly:

  1. Day 1-2: Adapt existing model or create simplified version (5-8 indicators)

  2. Day 3-4: Train field teams on data collection using mobile forms

  3. Day 5-7: Begin assessments in affected areas

  4. Day 8-10: Batch score and generate targeting list

  5. Day 11+: Begin assistance delivery while continuing assessments

Tools to prepare in advance:

  • [ ] Pre-configured vulnerability model

  • [ ] Mobile data collection form (ODK, KoboToolbox)

  • [ ] Field team training materials

  • [ ] Batch scoring procedure documentation

Best Practices#

  1. Context matters: Adapt indicators to local vulnerability drivers

  2. Update frequently: Vulnerability changes faster than poverty

  3. Combine data sources: Registration data + assessment + observation

  4. Validate with communities: Do classifications match local perceptions?

  5. Plan for shocks: Have simplified rapid-assessment models ready

  6. Track changes: Monitor how classifications shift over time

  7. Link to response: Vulnerability assessment must connect to assistance

  8. Protect data: Vulnerability information is highly sensitive

Security Considerations#

Vulnerability data can expose households to risk:

Risk

Mitigation

Targeting by armed groups

Anonymize geographic data, restrict access

Stigma

Use neutral terms, limit who sees detailed scores

Data breaches

Encrypt, limit export, audit access logs

Manipulation

Validate extreme scores, investigate suspicious patterns

Configure strict access controls in Settings → Users & Groups → Scoring Permissions.

Next Steps#

See also: