Analyze effective take-up rates across all benefit programs

Background

We currently seed benefit take-up rates in policyengine-uk-data based on prior studies. However, these rates change as a result of:

• Reweighting processes
• Integrating SPI (Survey of Personal Incomes) data

Current Analysis

We have an initial analysis for Universal Credit and Child Tax Credit:
https://gist.github.com/MaxGhenis/763db9278ddecdf310f160a73e138c8a

Request

We need a comprehensive analysis of effective take-up rates across all benefit programs in the UK model, including but not limited to:

• Universal Credit (UC)
• Child Tax Credit (CTC)
• Working Tax Credit (WTC)
• Pension Credit
• Housing Benefit
• Council Tax Support/Reduction
• Child Benefit
• Income Support
• Jobseeker's Allowance (JSA)
• Employment and Support Allowance (ESA)
• Personal Independence Payment (PIP)
• Disability Living Allowance (DLA)
• Attendance Allowance
• Carer's Allowance
• State Pension

Deliverables

1. Documentation of initial seeded take-up rates (from prior studies)
2. Calculation of effective take-up rates after reweighting and SPI integration
3. Comparison between seeded vs. effective rates
4. Analysis of how data processing steps affect take-up assumptions
5. Recommendations for any adjustments needed

This will help us understand how our data processing pipeline affects benefit modeling and ensure our simulations reflect realistic take-up patterns.

Start by reviewing the existing Universal Credit and Child Tax Credit analysis in the linked gist and the seeded rates in policyengine-uk-data. Map the reweighting and SPI integration steps across the listed benefit programs; done means documenting seeded and effective rates, comparing them, explaining processing effects, and recording recommendations.