Remove birth_year from FRS dataset generation

birth_year should be calculated from age and period in the model,
not stored as static data in the dataset. This allows birth_year to
properly update in multi-year projections.

With static birth_year in the dataset:
- 2026: birth_year stays 2006-2023 (based on 2023 survey)
- 2029: birth_year stays 2006-2023 (incorrect)

By calculating birth_year = period.year - age:
- 2026: birth_year becomes 2009-2026 (correct for 2026)
- 2029: birth_year becomes 2012-2029 (correct for 2029)

This fix is required for PolicyEngine/policyengine-uk#1352 to work
correctly and ensure two-child limit cost projections increase over
time as expected.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: MaxGhenis

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: patch
+  changes:
+    changed:
+    - Remove birth_year from FRS dataset generation to allow dynamic calculation

policyengine_uk_data/datasets/frs.py

@@ -110,7 +110,8 @@ def create_frs(
     # Add basic personal variables
     age = person.age80 + person.age
     pe_person["age"] = age
-    pe_person["birth_year"] = np.ones_like(person.age) * (year - age)
+    # birth_year should be calculated from age and period in the model,
+    # not stored as static data (see PolicyEngine/policyengine-uk#1352)
     # Age fields are AGE80 (top-coded) and AGE in the adult and child tables, respectively.
     pe_person["gender"] = np.where(person.sex == 1, "MALE", "FEMALE")
     pe_person["hours_worked"] = np.maximum(person.tothours, 0) * 52