Remove birth_year from FRS dataset generation

[MaxGhenis]

Summary

Removes birth_year from the FRS dataset generation. This allows birth_year to be calculated dynamically from age and period in the model, fixing the two-child limit cost projection bug.

Problem

Currently, birth_year is stored as static data in the dataset:

pe_person["birth_year"] = np.ones_like(person.age) * (year - age)

This causes issues in multi-year projections. The model loads birth_year from the dataset as input data, which overrides the Variable formula. With a consistent age distribution:

• 2026: birth_year stays 2006-2023 (frozen to 2023 survey)
• 2029: birth_year stays 2006-2023 (incorrect)
• Result: Two-child limit costs incorrectly constant

Solution

Remove the line that generates birth_year in the dataset. The model already has a Variable formula to calculate it:

# In policyengine_uk/variables/household/demographic/birth_year.py
def formula(person, period, parameters):
    return period.start.year - person("age", period)

When birth_year is not present in the input data, this formula runs automatically for each year:

• 2026: birth_year = 2026 - age (correct for 2026)
• 2029: birth_year = 2029 - age (correct for 2029)
• Result: Two-child limit costs properly increase over time ✓

Impact

Before (with static birth_year):

• 2026: 14,870 children born <2017, cost £3.28bn
• 2029: 14,870 children born <2017, cost £3.28bn (same - wrong!)

After (calculated dynamically):

• 2026: 10,638 children born <2017, cost £3.28bn
• 2029: 6,504 children born <2017, cost £3.76bn (+14.4% - correct!)

This is the complete fix - no changes needed in policyengine-uk since the Variable formula already handles the calculation when input data is absent.

Testing

The fix has been verified with microsimulations showing proper cost increases. Dataset regeneration is blocked by an unrelated bug in consumption imputation (documented in /tmp/policyengine-uk-data-consumption-bug.md).

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