Social Welfare Development Index (SWDI)#

This guide is for implementers configuring the Social Welfare Development Index (SWDI), a multi-dimensional welfare assessment framework. SWDI evaluates household wellbeing across multiple domains rather than focusing solely on poverty or vulnerability.

What is SWDI?#

The Social Welfare Development Index (SWDI) is a composite scoring method that:

  • Measures wellbeing across multiple dimensions (health, education, housing, economic security)

  • Aggregates sub-indices into a total welfare score

  • Provides a holistic view of household welfare beyond income

  • Identifies specific areas where households need support

SWDI is similar to HDI (Human Development Index) but adapted for household-level social protection targeting.

When to Use SWDI#

Use SWDI For

Don't Use SWDI For

Comprehensive welfare programs

Emergency cash transfers (too slow)

Multi-sectoral interventions

Single-purpose programs

Long-term development tracking

Quick targeting decisions

Identifying service gaps

Simple eligibility screening

Monitoring program impacts

Administrative census only

SWDI is best when: Your program can address multiple dimensions of wellbeing (health, education, housing, economic) and you want to track progress over time.

SWDI Structure#

A typical SWDI model has four main components:

Component

Weight

What It Measures

Health Index

30%

Health status, access to care, nutrition

Education Index

25%

School enrollment, educational attainment

Economic Security Index

25%

Income, employment, assets

Housing Index

20%

Housing quality, utilities, amenities

Each component is calculated separately as a sub-index (0-100 scale), then weighted and combined.

Step-by-Step Configuration#

Step 1: Create the SWDI Model#

Go to Scoring → Scoring Models and create:

Field

Value

Name

Social Welfare Development Index 2024

Code

SWDI_2024

Category

custom

Calculation Method

weighted_sum

Active

☑ (after configuration)

Effective Date

2024-01-01

Description

Multi-dimensional welfare assessment across health, education, economic, and housing domains

Note: We'll create the model in two stages:

  1. First, configure sub-indices as separate scoring models

  2. Then, create the main SWDI model that references them

Alternatively, use a single model with indicator grouping.

Approach 1: Single Integrated Model#

This approach uses one scoring model with organized indicators.

Step 2A: Health Index Indicators (Weight: 0.30)#

Health Facility Access#

Field

Value

Code

health_access

Name

Access to Health Facility

Field Path

household.health_facility_access

Weight

0.10

Calculation Type

mapped

Required

Yes

Sequence

1

Value Mappings:

Input Value

Output Score

Notes

regular_access

100

Can access healthcare when needed

limited_access

60

Sometimes face barriers

no_access

0

Cannot access healthcare

Household Illness Burden#

Field

Value

Code

chronic_illness

Name

Members with Chronic Illness

Field Path

household.chronic_illness_count

Weight

0.08

Calculation Type

range

Required

Yes

Sequence

2

Value Mappings (Range - Inverse Scoring):

Min

Max

Output Score

Notes

0

0

100

No chronic illness

1

1

60

One member

2

2

30

Two members

3

99

0

Three or more

Child Nutrition Status#

Field

Value

Code

child_nutrition

Name

Children Under 5 Well Nourished

Field Path

household.children_well_nourished

Weight

0.07

Calculation Type

mapped

Required

No

Default Value

True

Sequence

3

Value Mappings:

Input Value

Output Score

Notes

True

100

All children U5 well nourished

False

0

At least one child malnourished

How to determine: Based on growth monitoring, MUAC measurements, or self-reported assessment.

Health Insurance Coverage#

Field

Value

Code

health_insurance

Name

Household Has Health Insurance

Field Path

household.has_health_insurance

Weight

0.05

Calculation Type

mapped

Required

Yes

Sequence

4

Value Mappings:

Input Value

Output Score

True

100

False

0

Step 3A: Education Index Indicators (Weight: 0.25)#

School-Age Children Enrolled#

Field

Value

Code

school_enrollment

Name

School-Age Children Enrolled

Field Path

household.school_enrollment_rate

Weight

0.12

Calculation Type

range

Required

Yes

Sequence

5

Value Mappings (Range):

Min

Max

Output Score

Notes

100

100

100

All children enrolled

75

99

70

Most children enrolled

50

74

40

Half enrolled

1

49

20

Few enrolled

0

0

0

No enrollment

How to calculate: (Enrolled children ÷ Total school-age children) × 100

Use CEL formula:

(household.children_enrolled / household.children_school_age) * 100

Adult Literacy#

Field

Value

Code

adult_literacy

Name

Adult Literacy Rate in Household

Field Path

household.adult_literacy_rate

Weight

0.08

Calculation Type

range

Required

Yes

Sequence

6

Value Mappings (Range):

Min

Max

Output Score

100

100

100

75

99

75

50

74

50

25

49

25

0

24

0

How to calculate: (Literate adults ÷ Total adults) × 100

Highest Education in Household#

Field

Value

Code

max_education

Name

Highest Education Level in Household

Field Path

household.max_education_level

Weight

0.05

Calculation Type

mapped

Required

Yes

Sequence

7

Value Mappings:

Input Value

Output Score

tertiary

100

vocational

80

secondary

60

primary

30

none

0

Step 4A: Economic Security Index Indicators (Weight: 0.25)#

Employment Status#

Field

Value

Code

employment_rate

Name

Working-Age Adults Employed

Field Path

household.employment_rate

Weight

0.10

Calculation Type

range

Required

Yes

Sequence

8

Value Mappings (Range):

Min

Max

Output Score

Notes

75

100

100

Most/all adults working

50

74

70

Half employed

25

49

40

Few employed

1

24

20

Very low employment

0

0

0

No employment

How to calculate: (Employed adults ÷ Working-age adults) × 100

Income Stability#

Field

Value

Code

income_stability

Name

Regular Income Sources

Field Path

household.has_regular_income

Weight

0.08

Calculation Type

mapped

Required

Yes

Sequence

9

Value Mappings:

Input Value

Output Score

Notes

regular

100

Salary, pension, stable business

irregular

50

Casual labor, seasonal work

none

0

No regular income

Asset Ownership#

Field

Value

Code

productive_assets

Name

Owns Productive Assets

Field Path

household.productive_asset_score

Weight

0.07

Calculation Type

range

Required

Yes

Sequence

10

Value Mappings (Range):

Min

Max

Output Score

Notes

8

10

100

Multiple productive assets

5

7

70

Some productive assets

2

4

40

Few assets

0

1

0

No/minimal productive assets

Productive assets: Land, livestock, business equipment, tools, vehicles for work

How to score: Create an asset index (0-10) based on asset value/count.

Step 5A: Housing Index Indicators (Weight: 0.20)#

Housing Structure Quality#

Field

Value

Code

housing_quality

Name

Housing Quality Index

Field Path

household.housing_quality_score

Weight

0.08

Calculation Type

range

Required

Yes

Sequence

11

Value Mappings (Range):

Min

Max

Output Score

8

10

100

6

7

70

4

5

40

0

3

0

Housing quality score: Composite of roof, wall, floor materials (score each 0-10, average them)

Water & Sanitation#

Field

Value

Code

wash_access

Name

WASH Access Score

Field Path

household.wash_score

Weight

0.07

Calculation Type

range

Required

Yes

Sequence

12

Value Mappings (Range):

Min

Max

Output Score

8

10

100

5

7

60

2

4

30

0

1

0

WASH score: Combine water source (0-5) + sanitation facility (0-5) = 0-10 total

Electricity Access#

Field

Value

Code

electricity

Name

Has Reliable Electricity

Field Path

household.has_electricity

Weight

0.03

Calculation Type

mapped

Required

Yes

Sequence

13

Value Mappings:

Input Value

Output Score

Notes

grid_reliable

100

Connected to grid, reliable

grid_unreliable

70

Connected but frequent outages

off_grid

40

Solar, generator (limited)

none

0

No electricity

Overcrowding#

Field

Value

Code

persons_per_room

Name

Persons Per Sleeping Room

Field Path

household.persons_per_room

Weight

0.02

Calculation Type

range

Required

Yes

Sequence

14

Value Mappings (Range - Inverse):

Min

Max

Output Score

Notes

0

2

100

Not overcrowded

2.01

3

70

Slightly overcrowded

3.01

4

40

Overcrowded

4.01

99

0

Severely overcrowded

How to calculate: Total household members ÷ Number of sleeping rooms

Step 6A: Define SWDI Thresholds#

SWDI total score (0-100) classifications:

Min Score

Max Score

Classification Code

Classification Label

Color

0.0

30.0

VERY_LOW_WELFARE

Very Low Welfare

red

30.01

50.0

LOW_WELFARE

Low Welfare

orange

50.01

70.0

MODERATE_WELFARE

Moderate Welfare

yellow

70.01

85.0

HIGH_WELFARE

High Welfare

light green

85.01

100.0

VERY_HIGH_WELFARE

Very High Welfare

dark green

Step 7A: Validate and Test#

Check weight distribution:

  • Health indicators: 0.10 + 0.08 + 0.07 + 0.05 = 0.30 ✓

  • Education indicators: 0.12 + 0.08 + 0.05 = 0.25 ✓

  • Economic indicators: 0.10 + 0.08 + 0.07 = 0.25 ✓

  • Housing indicators: 0.08 + 0.07 + 0.03 + 0.02 = 0.20 ✓

  • Total: 1.00 ✓

Test with sample household:

Test Case: Moderate Welfare Household#

Indicator

Value

Score

Weight

Weighted

Health (0.30)

Health Access

limited_access

60

0.10

6.0

Chronic Illness

1 member

60

0.08

4.8

Child Nutrition

True

100

0.07

7.0

Health Insurance

False

0

0.05

0.0

Education (0.25)

School Enrollment

100%

100

0.12

12.0

Adult Literacy

75%

75

0.08

6.0

Max Education

secondary

60

0.05

3.0

Economic (0.25)

Employment Rate

60%

70

0.10

7.0

Income Stability

irregular

50

0.08

4.0

Productive Assets

3 assets

40

0.07

2.8

Housing (0.20)

Housing Quality

7/10

70

0.08

5.6

WASH Access

6/10

60

0.07

4.2

Electricity

grid_unreliable

70

0.03

2.1

Persons Per Room

2.5

70

0.02

1.4

Total

1.00

65.9

Final Score: 65.9 → MODERATE_WELFARE ✓

Approach 2: Modular Sub-Index Models#

For more flexibility, create separate scoring models for each domain, then combine them.

Step 2B: Create Sub-Index Models#

Create four separate scoring models:

  1. Health Index (SWDI_HEALTH_2024)

  2. Education Index (SWDI_EDU_2024)

  3. Economic Index (SWDI_ECON_2024)

  4. Housing Index (SWDI_HOUSING_2024)

Each sub-index model:

  • Has its own indicators

  • Produces a 0-100 score

  • Can be updated independently

Step 3B: Create Composite SWDI Model#

Use CEL formula to combine sub-indices:

Field

Value

Name

SWDI Composite 2024

Code

SWDI_2024

Calculation Method

cel_formula

CEL Expression

See below

CEL Formula:

(health_score.score * 0.30) +
(education_score.score * 0.25) +
(economic_score.score * 0.25) +
(housing_score.score * 0.20)

Variables needed:

  • health_score → Latest score from SWDI_HEALTH_2024 model

  • education_score → Latest score from SWDI_EDU_2024 model

  • economic_score → Latest score from SWDI_ECON_2024 model

  • housing_score → Latest score from SWDI_HOUSING_2024 model

Advantage: You can update education indicators without re-configuring health, economic, or housing components.

Using SWDI Results#

Comprehensive Program Targeting#

Target households with low SWDI scores for multi-sectoral support:

SWDI Score

Intervention Package

0-30

Intensive case management + cash + services

30-50

Cash transfer + service referrals

50-70

Service access support

70+

Monitoring only (graduated)

Domain-Specific Interventions#

Use sub-index scores to identify specific needs:

Example: Household with:

  • Health Index: 40 (low) → Refer to health services, insurance enrollment

  • Education Index: 85 (high) → No education intervention needed

  • Economic Index: 50 (moderate) → Livelihood support

  • Housing Index: 30 (low) → Housing upgrade assistance

Progress Monitoring#

Re-score households annually to track welfare changes:

Year

SWDI Score

Classification

Change

2024

45

Low Welfare

Baseline

2025

58

Moderate Welfare

+13 (improved)

2026

72

High Welfare

+14 (graduated)

Graduation Criteria#

Set graduation thresholds:

  • Graduate when: SWDI ≥ 70 for two consecutive years

  • Reduce support when: SWDI 50-70

  • Intensive support when: SWDI < 50

Adapting SWDI to Your Context#

SWDI is highly customizable:

Adjust Domain Weights#

Example 1: Education-Focused Program

  • Health: 20%

  • Education: 40% (increased)

  • Economic: 20%

  • Housing: 20%

Example 2: Post-Disaster Context

  • Health: 35% (increased)

  • Education: 15% (decreased)

  • Economic: 30%

  • Housing: 20%

Add Custom Domains#

Consider adding:

  • Social Cohesion Index (10-15%) - Community participation, social support

  • Security Index (10-15%) - Safety, protection concerns

  • Environmental Index (5-10%) - Climate resilience, natural resource access

Simplified SWDI#

For rapid assessment, reduce indicators:

  • 2-3 indicators per domain (8-12 total)

  • Use observable indicators only (no calculated fields)

  • Binary scoring where possible (0 or 100)

Integration with Programs#

Eligibility Based on SWDI#

In eligibility manager:

Field

Value

Scoring Model

SWDI_2024

Maximum Score

50.0

Households with SWDI ≤ 50 are eligible.

Combining SWDI with Other Scores#

Use multiple scoring criteria:

(swdi_score.score <= 50) ||
(pmt_score.classification == 'POOR') ||
(vuln_score.classification == 'HIGH_VULN')

Households qualify if they meet ANY of these conditions.

Benefit Levels by Domain Deficits#

Customize benefit packages based on which domains are low:

Domain Deficits

Benefit Package

Health only

Health insurance + service vouchers

Education only

School supplies + tutoring

Economic only

Cash grant + livelihood training

Housing only

Housing upgrade grant

Multiple domains

Comprehensive support package

Common Patterns#

Pattern 1: Development Program with Graduation#

  • Entry criteria: SWDI < 50

  • Support: Multi-sectoral interventions over 2-3 years

  • Graduation: SWDI ≥ 70 for 2 consecutive assessments

  • Re-assess: Annually

Pattern 2: Service Gap Identification#

  • Calculate: SWDI and sub-indices for entire registry

  • Analyze: Which domains have lowest average scores?

  • Plan: Invest in services for weakest domains

  • Monitor: Track domain scores over time

Pattern 3: Targeting with Flexibility#

  • Primary criterion: SWDI < 60

  • Secondary: Allow case-by-case inclusion for households with specific vulnerabilities (e.g., disability, recent shock)

  • Appeals: Use SWDI breakdown to review cases

Are You Stuck?#

SWDI seems too complex? Start with a simplified version. Use 2-3 indicators per domain. You can add more sophistication later.

How to weight domains? Base weights on your program objectives. If you're a health program, weight health more heavily. If comprehensive, use balanced weights (20-30% each).

Sub-indices not calculating? Check that indicators within each domain have weights that make sense. Each sub-index should produce a 0-100 score.

Scores don't match field observations? SWDI is multi-dimensional—a household can score high overall but have specific domain weaknesses. Review sub-index scores, not just total.

How often to recalculate? SWDI components change slowly. Annual assessment is typical. More frequent (quarterly) if you're actively intervening.

Should all indicators be weighted equally within domains? No. Weight indicators within each domain based on their importance. Health insurance might be less critical than health facility access, so use lower weight.

Can I use SWDI with missing data? Mark non-critical indicators as not required. Calculate SWDI based on available indicators, but note that missing data affects comparability.

Best Practices#

  1. Align with program goals: Weight domains based on what your program addresses

  2. Use existing data: Where possible, map to data you already collect

  3. Validate with communities: Do SWDI classifications match local perceptions?

  4. Track over time: SWDI's strength is monitoring change

  5. Report by domain: Don't just share total scores—show domain breakdowns

  6. Update periodically: Review indicator relevance every 2-3 years

  7. Combine with qualitative data: SWDI + case worker observations = best picture

  8. Set realistic targets: Not all households will reach 100—what's "good enough"?

Security Considerations#

SWDI data is comprehensive and sensitive:

Data Type

Risk

Mitigation

Health information

Medical privacy

Limit access to health sub-index details

Housing details

Security risk (valuable assets)

Aggregate reporting, anonymize locations

Education data

Stigma (low education)

Present as household average, not individuals

Economic data

Targeting for exploitation

Encrypt, restrict export, audit access

Next Steps#

See also: