diff --git a/.github/workflows/local_area_promote.yaml b/.github/workflows/local_area_promote.yaml
new file mode 100644
index 00000000..8d7d235e
--- /dev/null
+++ b/.github/workflows/local_area_promote.yaml
@@ -0,0 +1,43 @@
+name: Promote Local Area H5 Files
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Version to promote (e.g. 1.23.0)'
+        required: true
+        type: string
+      branch:
+        description: 'Branch to use for repo setup'
+        required: false
+        default: 'main'
+        type: string
+
+jobs:
+  promote-local-area:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    env:
+      HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }}
+      MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }}
+      MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }}
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.13'
+
+      - name: Install Modal CLI
+        run: pip install modal
+
+      - name: Promote staged files to production
+        run: |
+          VERSION="${{ github.event.inputs.version }}"
+          BRANCH="${{ github.event.inputs.branch }}"
+          echo "Promoting version ${VERSION} from branch ${BRANCH}"
+          modal run modal_app/local_area.py::main_promote --version="${VERSION}" --branch="${BRANCH}"
diff --git a/.github/workflows/local_area_publish.yaml b/.github/workflows/local_area_publish.yaml
index 7e756ad8..44675e63 100644
--- a/.github/workflows/local_area_publish.yaml
+++ b/.github/workflows/local_area_publish.yaml
@@ -49,7 +49,7 @@ jobs:
       - name: Install Modal CLI
         run: pip install modal
 
-      - name: Run local area publishing on Modal
+      - name: Run local area build and stage on Modal
         run: |
           NUM_WORKERS="${{ github.event.inputs.num_workers || '8' }}"
           SKIP_UPLOAD="${{ github.event.inputs.skip_upload || 'false' }}"
@@ -63,3 +63,13 @@ jobs:
 
           echo "Running: $CMD"
           $CMD
+
+      - name: Post-build summary
+        if: success()
+        run: |
+          echo "## Build + Stage Complete" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "Files have been uploaded to GCS and staged on HuggingFace." >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "### Next step: Promote to production" >> $GITHUB_STEP_SUMMARY
+          echo "Trigger the **Promote Local Area H5 Files** workflow with the version from the build output." >> $GITHUB_STEP_SUMMARY
diff --git a/Makefile b/Makefile
index c538a7bc..09d984a9 100644
--- a/Makefile
+++ b/Makefile
@@ -87,7 +87,7 @@ data: download
 	python policyengine_us_data/datasets/cps/extended_cps.py
 	python policyengine_us_data/datasets/cps/enhanced_cps.py
 	python policyengine_us_data/datasets/cps/small_enhanced_cps.py
-	python policyengine_us_data/datasets/cps/local_area_calibration/create_stratified_cps.py 10500
+	python policyengine_us_data/datasets/cps/local_area_calibration/create_stratified_cps.py 12000 --top=99.5 --seed=3526
 
 publish-local-area:
 	python policyengine_us_data/datasets/cps/local_area_calibration/publish_local_area.py
diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29b..c69b4573 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,6 @@
+- date: 2026-02-02
+  type: fixed
+  description: Fix stale calibration targets by deriving time_period from dataset across all ETL scripts, using income_tax_positive for CBO calibration, and adding 119th Congress district code support for consistent redistricting alignment
+- date: 2026-02-07
+  type: added
+  description: Add ACA Premium Tax Credit targets from IRS SOI data (cherry-picked from PR #508)
diff --git a/modal_app/local_area.py b/modal_app/local_area.py
index d10273d5..92e06833 100644
--- a/modal_app/local_area.py
+++ b/modal_app/local_area.py
@@ -259,14 +259,11 @@ def validate_staging(branch: str, version: str) -> Dict:
     memory=8192,
     timeout=14400,
 )
-def atomic_upload(branch: str, version: str, manifest: Dict) -> str:
+def upload_to_staging(branch: str, version: str, manifest: Dict) -> str:
     """
-    Upload files using staging approach for atomic deployment.
+    Upload files to GCS (production) and HuggingFace (staging only).
 
-    1. Upload to GCS (direct, overwrites existing)
-    2. Upload to HuggingFace staging/ folder
-    3. Atomically promote staging/ to production paths
-    4. Clean up staging/
+    Promote must be run separately via promote_publish.
     """
     setup_gcp_credentials()
     setup_repo(branch)
@@ -286,8 +283,6 @@ def atomic_upload(branch: str, version: str, manifest: Dict) -> str:
 from policyengine_us_data.utils.data_upload import (
     upload_local_area_file,
     upload_to_staging_hf,
-    promote_staging_to_production_hf,
-    cleanup_staging_hf,
 )
 
 manifest = json.loads('''{manifest_json}''')
@@ -306,11 +301,9 @@ def atomic_upload(branch: str, version: str, manifest: Dict) -> str:
 print(f"Verified {{verification['verified']}} files")
 
 files_with_paths = []
-rel_paths = []
 for rel_path in manifest["files"].keys():
     local_path = version_dir / rel_path
     files_with_paths.append((local_path, rel_path))
-    rel_paths.append(rel_path)
 
 # Upload to GCS (direct to production paths)
 print(f"Uploading {{len(files_with_paths)}} files to GCS...")
@@ -331,12 +324,73 @@ def atomic_upload(branch: str, version: str, manifest: Dict) -> str:
 hf_count = upload_to_staging_hf(files_with_paths, version)
 print(f"Uploaded {{hf_count}} files to HuggingFace staging/")
 
-# Atomically promote staging to production
-print("Promoting staging/ to production (atomic commit)...")
+print(f"Staged version {{version}} for promotion")
+""",
+        ],
+        text=True,
+        env=os.environ.copy(),
+    )
+
+    if result.returncode != 0:
+        raise RuntimeError(f"Upload failed: {result.stderr}")
+
+    return (
+        f"Staged version {version} with {len(manifest['files'])} files. "
+        f"Run promote workflow to publish to HuggingFace production."
+    )
+
+
+@app.function(
+    image=image,
+    secrets=[hf_secret],
+    volumes={VOLUME_MOUNT: staging_volume},
+    memory=4096,
+    timeout=3600,
+)
+def promote_publish(branch: str = "main", version: str = "") -> str:
+    """
+    Promote staged files from HF staging/ to production paths, then cleanup.
+
+    Reads the manifest from the Modal staging volume to determine which
+    files to promote.
+    """
+    setup_repo(branch)
+
+    staging_dir = Path(VOLUME_MOUNT)
+    staging_volume.reload()
+
+    manifest_path = staging_dir / version / "manifest.json"
+    if not manifest_path.exists():
+        raise RuntimeError(
+            f"No manifest found at {manifest_path}. "
+            f"Run build+stage workflow first."
+        )
+
+    with open(manifest_path) as f:
+        manifest = json.load(f)
+
+    rel_paths_json = json.dumps(list(manifest["files"].keys()))
+
+    result = subprocess.run(
+        [
+            "uv",
+            "run",
+            "python",
+            "-c",
+            f"""
+import json
+from policyengine_us_data.utils.data_upload import (
+    promote_staging_to_production_hf,
+    cleanup_staging_hf,
+)
+
+rel_paths = json.loads('''{rel_paths_json}''')
+version = "{version}"
+
+print(f"Promoting {{len(rel_paths)}} files from staging/ to production...")
 promoted = promote_staging_to_production_hf(rel_paths, version)
 print(f"Promoted {{promoted}} files to production")
 
-# Clean up staging
 print("Cleaning up staging/...")
 cleaned = cleanup_staging_hf(rel_paths, version)
 print(f"Cleaned up {{cleaned}} files from staging/")
@@ -349,9 +403,9 @@ def atomic_upload(branch: str, version: str, manifest: Dict) -> str:
     )
 
     if result.returncode != 0:
-        raise RuntimeError(f"Upload failed: {result.stderr}")
+        raise RuntimeError(f"Promote failed: {result.stderr}")
 
-    return f"Successfully published version {version} with {len(manifest['files'])} files"
+    return f"Successfully promoted version {version} with {len(manifest['files'])} files"
 
 
 @app.function(
@@ -544,10 +598,24 @@ def coordinate_publish(
             f"WARNING: Expected {expected_total} files, found {actual_total}"
         )
 
-    print("\nStarting atomic upload...")
-    result = atomic_upload.remote(
+    print("\nStarting upload to staging...")
+    result = upload_to_staging.remote(
         branch=branch, version=version, manifest=manifest
     )
+    print(result)
+
+    print("\n" + "=" * 60)
+    print("BUILD + STAGE COMPLETE")
+    print("=" * 60)
+    print(
+        f"To promote to HuggingFace production, run the "
+        f"'Promote Local Area H5 Files' workflow with version={version}"
+    )
+    print(
+        "Or run manually: modal run modal_app/local_area.py::main_promote "
+        f"--version={version}"
+    )
+    print("=" * 60)
 
     return result
 
@@ -565,3 +633,15 @@ def main(
         skip_upload=skip_upload,
     )
     print(result)
+
+
+@app.local_entrypoint()
+def main_promote(
+    version: str = "",
+    branch: str = "main",
+):
+    """Promote staged files to HuggingFace production."""
+    if not version:
+        raise ValueError("--version is required")
+    result = promote_publish.remote(branch=branch, version=version)
+    print(result)
diff --git a/policyengine_us_data/datasets/cps/enhanced_cps.py b/policyengine_us_data/datasets/cps/enhanced_cps.py
index 9799e99a..385ec1e9 100644
--- a/policyengine_us_data/datasets/cps/enhanced_cps.py
+++ b/policyengine_us_data/datasets/cps/enhanced_cps.py
@@ -196,7 +196,7 @@ def generate(self):
                 loss_matrix_clean,
                 targets_array_clean,
                 log_path="calibration_log.csv",
-                epochs=200,
+                epochs=250,
                 seed=1456,
             )
             data["household_weight"][year] = optimised_weights
diff --git a/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py b/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py
index aa954aba..c27cf3e0 100644
--- a/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py
+++ b/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py
@@ -252,39 +252,24 @@ def get_pseudo_input_variables(sim) -> set:
     """
     Identify pseudo-input variables that should NOT be saved to H5 files.
 
-    A pseudo-input is a variable that:
-    - Appears in sim.input_variables (has stored values)
-    - Has 'adds' or 'subtracts' attribute
-    - At least one component has a formula (is calculated)
-
-    These variables have stale pre-computed values that corrupt calculations
-    when reloaded, because the stored value overrides the formula.
+    NOTE: This function currently returns an empty set. The original logic
+    excluded variables with 'adds' or 'subtracts' attributes, but analysis
+    showed that in CPS data, these variables contain authoritative stored
+    data that does NOT match their component variables:
+
+    - pre_tax_contributions: components are all 0, aggregate has imputed values
+    - tax_exempt_pension_income: aggregate has 135M, components only 20M
+    - taxable_pension_income: aggregate has 82M, components only 29M
+    - interest_deduction: aggregate has 41M, components are 0
+
+    The 'adds' attribute defines how to CALCULATE these values, but in CPS
+    data the stored values are the authoritative source. Excluding them and
+    recalculating from components produces incorrect results.
+
+    For geo-stacking, entity ID reindexing preserves within-entity
+    relationships, so aggregation within a person or tax_unit remains valid.
     """
-    tbs = sim.tax_benefit_system
-    pseudo_inputs = set()
-
-    for var_name in sim.input_variables:
-        var = tbs.variables.get(var_name)
-        if not var:
-            continue
-
-        adds = getattr(var, "adds", None)
-        if adds and isinstance(adds, list):
-            for component in adds:
-                comp_var = tbs.variables.get(component)
-                if comp_var and len(getattr(comp_var, "formulas", {})) > 0:
-                    pseudo_inputs.add(var_name)
-                    break
-
-        subtracts = getattr(var, "subtracts", None)
-        if subtracts and isinstance(subtracts, list):
-            for component in subtracts:
-                comp_var = tbs.variables.get(component)
-                if comp_var and len(getattr(comp_var, "formulas", {})) > 0:
-                    pseudo_inputs.add(var_name)
-                    break
-
-    return pseudo_inputs
+    return set()
 
 
 def apply_op(values: np.ndarray, op: str, val: str) -> np.ndarray:
diff --git a/policyengine_us_data/db/DATABASE_GUIDE.md b/policyengine_us_data/db/DATABASE_GUIDE.md
index ac038cb7..a9f8f398 100644
--- a/policyengine_us_data/db/DATABASE_GUIDE.md
+++ b/policyengine_us_data/db/DATABASE_GUIDE.md
@@ -110,7 +110,7 @@ The `stratum_group_id` field categorizes strata:
 | 5 | Medicaid | Medicaid enrollment strata |
 | 6 | EITC | EITC recipients by qualifying children |
 | 7 | State Income Tax | State-level income tax collections (Census STC) |
-| 100-118 | IRS Conditional | Each IRS variable paired with conditional count constraints |
+| 100-119 | IRS Conditional | Each IRS variable paired with conditional count constraints (includes ACA PTC at 119) |
 
 ### Conditional Strata (IRS SOI)
 
diff --git a/policyengine_us_data/db/create_initial_strata.py b/policyengine_us_data/db/create_initial_strata.py
index f3edb1b4..8dda76e2 100644
--- a/policyengine_us_data/db/create_initial_strata.py
+++ b/policyengine_us_data/db/create_initial_strata.py
@@ -1,3 +1,4 @@
+import argparse
 import logging
 from typing import Dict
 
@@ -6,6 +7,8 @@
 from sqlmodel import Session, create_engine
 
 from policyengine_us_data.storage import STORAGE_FOLDER
+
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
@@ -68,6 +71,28 @@ def fetch_congressional_districts(year):
 
 
 def main():
+    parser = argparse.ArgumentParser(
+        description="Create initial geographic strata for calibration"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The year for Census API calls is derived from the dataset's "
+            "default_calculation_period. Default: %(default)s"
+        ),
+    )
+    args = parser.parse_args()
+
+    # Derive year from dataset
+    from policyengine_us import Microsimulation
+
+    print(f"Loading dataset: {args.dataset}")
+    sim = Microsimulation(dataset=args.dataset)
+    year = int(sim.default_calculation_period)
+    print(f"Derived year from dataset: {year}")
+
     # State FIPS to name/abbreviation mapping
     STATE_NAMES = {
         1: "Alabama (AL)",
@@ -123,8 +148,7 @@ def main():
         56: "Wyoming (WY)",
     }
 
-    # Fetch congressional district data for year 2023
-    year = 2023
+    # Fetch congressional district data
     cd_df = fetch_congressional_districts(year)
 
     DATABASE_URL = (
diff --git a/policyengine_us_data/db/etl_age.py b/policyengine_us_data/db/etl_age.py
index 39ffedf2..2e213d92 100644
--- a/policyengine_us_data/db/etl_age.py
+++ b/policyengine_us_data/db/etl_age.py
@@ -1,9 +1,13 @@
+import argparse
+
 import pandas as pd
 import numpy as np
 from sqlmodel import Session, create_engine, select
 
 from policyengine_us_data.storage import STORAGE_FOLDER
 
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
+
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
@@ -66,9 +70,12 @@ def transform_age_data(age_data, docs):
     df = df.rename({"GEO_ID": "ucgid_str"}, axis=1)
     df_data = df.rename(columns=rename_mapping)[["ucgid_str"] + list(AGE_COLS)]
 
-    # Filter out Puerto Rico's district and state records, if needed
+    # Filter out Puerto Rico's district and state records
+    # 5001800US7298 = 118th Congress, 5001900US7298 = 119th Congress
     df_geos = df_data[
-        ~df_data["ucgid_str"].isin(["5001800US7298", "0400000US72"])
+        ~df_data["ucgid_str"].isin(
+            ["5001800US7298", "5001900US7298", "0400000US72"]
+        )
     ].copy()
 
     df = df_geos[["ucgid_str"] + AGE_COLS]
@@ -279,10 +286,30 @@ def load_age_data(df_long, geo, year):
         session.commit()
 
 
-if __name__ == "__main__":
+def main():
+    parser = argparse.ArgumentParser(
+        description="ETL for age calibration targets"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The year for Census API calls is derived from the dataset's "
+            "default_calculation_period. Default: %(default)s"
+        ),
+    )
+    args = parser.parse_args()
+
+    # Derive year from dataset
+    from policyengine_us import Microsimulation
+
+    print(f"Loading dataset: {args.dataset}")
+    sim = Microsimulation(dataset=args.dataset)
+    year = int(sim.default_calculation_period)
+    print(f"Derived year from dataset: {year}")
 
     # --- ETL: Extract, Transform, Load ----
-    year = 2023
 
     # ---- Extract ----------
     docs = get_census_docs(year)
@@ -301,3 +328,7 @@ def load_age_data(df_long, geo, year):
     load_age_data(long_national_df, "National", year)
     load_age_data(long_state_df, "State", year)
     load_age_data(long_district_df, "District", year)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/policyengine_us_data/db/etl_irs_soi.py b/policyengine_us_data/db/etl_irs_soi.py
index ed4da4e5..5f191ce3 100644
--- a/policyengine_us_data/db/etl_irs_soi.py
+++ b/policyengine_us_data/db/etl_irs_soi.py
@@ -1,3 +1,4 @@
+import argparse
 import logging
 from typing import Optional
 
@@ -7,6 +8,11 @@
 from sqlmodel import Session, create_engine, select
 
 from policyengine_us_data.storage import STORAGE_FOLDER
+
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
+
+# IRS SOI data is typically available ~2 years after the tax year
+IRS_SOI_LAG_YEARS = 2
 from policyengine_us_data.utils.raw_cache import (
     is_cached,
     cache_path,
@@ -281,6 +287,7 @@ def transform_soi_data(raw_df):
         dict(code="18425", name="salt", breakdown=None),
         dict(code="06500", name="income_tax", breakdown=None),
         dict(code="05800", name="income_tax_before_credits", breakdown=None),
+        dict(code="85530", name="aca_ptc", breakdown=None),
     ]
 
     # National ---------------
@@ -566,6 +573,28 @@ def load_soi_data(long_dfs, year):
         units="dollars",
     )
 
+    # ACA Premium Tax Credit
+    ptc_group = get_or_create_variable_group(
+        session,
+        name="aca_ptc_recipients",
+        category="tax",
+        is_histogram=False,
+        is_exclusive=False,
+        aggregation_method="sum",
+        display_order=9,
+        description="ACA Premium Tax Credit recipients and amounts",
+    )
+
+    get_or_create_variable_metadata(
+        session,
+        variable="aca_ptc",
+        group=ptc_group,
+        display_name="Premium Tax Credit",
+        display_order=1,
+        units="dollars",
+        notes="ACA Premium Tax Credit amount from IRS SOI",
+    )
+
     # Fetch existing geographic strata
     geo_strata = get_geographic_strata(session)
 
@@ -1207,9 +1236,40 @@ def load_soi_data(long_dfs, year):
 
 
 def main():
-    # NOTE: predates the finalization of the 2020 Census redistricting
-    # and there is district mapping in the Transform step
-    year = 2022
+    parser = argparse.ArgumentParser(
+        description="ETL for IRS SOI calibration targets"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The year for IRS SOI data is derived from the dataset's "
+            "default_calculation_period minus IRS_SOI_LAG_YEARS. "
+            "Default: %(default)s"
+        ),
+    )
+    parser.add_argument(
+        "--lag",
+        type=int,
+        default=IRS_SOI_LAG_YEARS,
+        help=(
+            "Years to subtract from dataset year for IRS SOI data "
+            "(default: %(default)s, since IRS data is ~2 years behind)"
+        ),
+    )
+    args = parser.parse_args()
+
+    # Derive year from dataset with lag applied
+    from policyengine_us import Microsimulation
+
+    print(f"Loading dataset: {args.dataset}")
+    sim = Microsimulation(dataset=args.dataset)
+    dataset_year = int(sim.default_calculation_period)
+    year = dataset_year - args.lag
+    print(
+        f"Dataset year: {dataset_year}, IRS SOI year: {year} (lag={args.lag})"
+    )
 
     # Extract -----------------------
     raw_df = extract_soi_data()
diff --git a/policyengine_us_data/db/etl_medicaid.py b/policyengine_us_data/db/etl_medicaid.py
index ed184144..435ccd42 100644
--- a/policyengine_us_data/db/etl_medicaid.py
+++ b/policyengine_us_data/db/etl_medicaid.py
@@ -1,3 +1,4 @@
+import argparse
 import logging
 
 import requests
@@ -7,6 +8,8 @@
 
 from policyengine_us_data.storage import STORAGE_FOLDER
 
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
+
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
@@ -325,7 +328,27 @@ def load_medicaid_data(long_state, long_cd, year):
 
 
 def main():
-    year = 2024
+    parser = argparse.ArgumentParser(
+        description="ETL for Medicaid calibration targets"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The year for targets is derived from the dataset's "
+            "default_calculation_period. Default: %(default)s"
+        ),
+    )
+    args = parser.parse_args()
+
+    # Derive year from dataset
+    from policyengine_us import Microsimulation
+
+    print(f"Loading dataset: {args.dataset}")
+    sim = Microsimulation(dataset=args.dataset)
+    year = int(sim.default_calculation_period)
+    print(f"Derived year from dataset: {year}")
 
     # Extract ------------------------------
     state_admin_df = extract_administrative_medicaid_data(year)
diff --git a/policyengine_us_data/db/etl_national_targets.py b/policyengine_us_data/db/etl_national_targets.py
index 7e02d6f0..fd97b83f 100644
--- a/policyengine_us_data/db/etl_national_targets.py
+++ b/policyengine_us_data/db/etl_national_targets.py
@@ -1,3 +1,5 @@
+import argparse
+
 from sqlmodel import Session, create_engine
 import pandas as pd
 
@@ -12,11 +14,19 @@
     get_or_create_source,
 )
 
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
+
 
-def extract_national_targets():
+def extract_national_targets(dataset: str = DEFAULT_DATASET):
     """
     Extract national calibration targets from various sources.
 
+    Parameters
+    ----------
+    dataset : str
+        Path to the calibration dataset (local path or HuggingFace URL).
+        The time period is derived from the dataset's default_calculation_period.
+
     Returns
     -------
     dict
@@ -26,18 +36,17 @@ def extract_national_targets():
         - conditional_count_targets: Enrollment counts requiring constraints
         - cbo_targets: List of CBO projection targets
         - treasury_targets: List of Treasury/JCT targets
+        - time_period: The year derived from the dataset
     """
-
-    # Initialize PolicyEngine for parameter access
     from policyengine_us import Microsimulation
 
-    sim = Microsimulation(
-        dataset="hf://policyengine/policyengine-us-data/cps_2023.h5"
-    )
+    print(f"Loading dataset: {dataset}")
+    sim = Microsimulation(dataset=dataset)
+
+    time_period = int(sim.default_calculation_period)
+    print(f"Derived time_period from dataset: {time_period}")
 
-    # Direct sum targets - these are regular variables that can be summed
-    # Store with their actual source year (2024 for hardcoded values from loss.py)
-    HARDCODED_YEAR = 2024
+    # Direct sum targets - use the time_period derived from the dataset
 
     # Separate tax-related targets that need filer constraint
     tax_filer_targets = [
@@ -46,35 +55,35 @@ def extract_national_targets():
             "value": 21.247e9,
             "source": "Joint Committee on Taxation",
             "notes": "SALT deduction tax expenditure",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "medical_expense_deduction",
             "value": 11.4e9,
             "source": "Joint Committee on Taxation",
             "notes": "Medical expense deduction tax expenditure",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "charitable_deduction",
             "value": 65.301e9,
             "source": "Joint Committee on Taxation",
             "notes": "Charitable deduction tax expenditure",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "interest_deduction",
             "value": 24.8e9,
             "source": "Joint Committee on Taxation",
             "notes": "Mortgage interest deduction tax expenditure",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "qualified_business_income_deduction",
             "value": 63.1e9,
             "source": "Joint Committee on Taxation",
             "notes": "QBI deduction tax expenditure",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
     ]
 
@@ -84,112 +93,112 @@ def extract_national_targets():
             "value": 13e9,
             "source": "Survey-reported (post-TCJA grandfathered)",
             "notes": "Alimony received - survey reported, not tax-filer restricted",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "alimony_expense",
             "value": 13e9,
             "source": "Survey-reported (post-TCJA grandfathered)",
             "notes": "Alimony paid - survey reported, not tax-filer restricted",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "medicaid",
             "value": 871.7e9,
             "source": "https://www.cms.gov/files/document/highlights.pdf",
             "notes": "CMS 2023 highlights document - total Medicaid spending",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "net_worth",
             "value": 160e12,
             "source": "Federal Reserve SCF",
             "notes": "Total household net worth",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "health_insurance_premiums_without_medicare_part_b",
             "value": 385e9,
             "source": "MEPS/NHEA",
             "notes": "Health insurance premiums excluding Medicare Part B",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "other_medical_expenses",
             "value": 278e9,
             "source": "MEPS/NHEA",
             "notes": "Out-of-pocket medical expenses",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "medicare_part_b_premiums",
             "value": 112e9,
             "source": "CMS Medicare data",
             "notes": "Medicare Part B premium payments",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "over_the_counter_health_expenses",
             "value": 72e9,
             "source": "Consumer Expenditure Survey",
             "notes": "OTC health products and supplies",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "child_support_expense",
             "value": 33e9,
             "source": "Census Bureau",
             "notes": "Child support payments",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "child_support_received",
             "value": 33e9,
             "source": "Census Bureau",
             "notes": "Child support received",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "spm_unit_capped_work_childcare_expenses",
             "value": 348e9,
             "source": "Census Bureau SPM",
             "notes": "Work and childcare expenses for SPM",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "spm_unit_capped_housing_subsidy",
             "value": 35e9,
             "source": "HUD/Census",
             "notes": "Housing subsidies",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "tanf",
             "value": 9e9,
             "source": "HHS/ACF",
             "notes": "TANF cash assistance",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "real_estate_taxes",
             "value": 500e9,
             "source": "Census Bureau",
             "notes": "Property taxes paid",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "rent",
             "value": 735e9,
             "source": "Census Bureau/BLS",
             "notes": "Rental payments",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "tip_income",
             "value": 53.2e9,
             "source": "IRS Form W-2 Box 7 statistics",
             "notes": "Social security tips uprated 40% to account for underreporting",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         # SSA benefit-type totals derived from trust fund data and
         # SSA fact sheet type shares
@@ -198,28 +207,28 @@ def extract_national_targets():
             "value": 1_060e9,
             "source": "https://www.ssa.gov/OACT/STATS/table4a3.html",
             "notes": "~73% of total OASDI ($1,452B CBO projection)",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "social_security_disability",
             "value": 148e9,
             "source": "https://www.ssa.gov/OACT/STATS/table4a3.html",
             "notes": "~10.2% of total OASDI (disabled workers)",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "social_security_survivors",
             "value": 160e9,
             "source": "https://www.ssa.gov/OACT/FACTS/",
             "notes": "~11.0% of total OASDI (widows, children of deceased)",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "social_security_dependents",
             "value": 84e9,
             "source": "https://www.ssa.gov/OACT/FACTS/",
             "notes": "~5.8% of total OASDI (spouses/children of retired+disabled)",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         # IRA contribution totals from IRS SOI accumulation tables
         {
@@ -227,14 +236,14 @@ def extract_national_targets():
             "value": 25e9,
             "source": "https://www.irs.gov/statistics/soi-tax-stats-accumulation-and-distribution-of-individual-retirement-arrangements",
             "notes": "Tax year 2022 (~5M x $4,510 avg) uprated ~12% to 2024",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "variable": "roth_ira_contributions",
             "value": 39e9,
             "source": "https://www.irs.gov/statistics/soi-tax-stats-accumulation-and-distribution-of-individual-retirement-arrangements",
             "notes": "Tax year 2022 (~10M x $3,482 avg) uprated ~12% to 2024",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
     ]
 
@@ -247,7 +256,7 @@ def extract_national_targets():
             "person_count": 72_429_055,
             "source": "CMS/HHS administrative data",
             "notes": "Medicaid enrollment count",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
         {
             "constraint_variable": "aca_ptc",
@@ -255,7 +264,7 @@ def extract_national_targets():
             "person_count": 19_743_689,
             "source": "CMS marketplace data",
             "notes": "ACA Premium Tax Credit recipients",
-            "year": HARDCODED_YEAR,
+            "year": time_period,
         },
     ]
 
@@ -302,8 +311,7 @@ def extract_national_targets():
 
     conditional_count_targets.extend(ssn_none_targets_by_year)
 
-    # CBO projection targets - get for a specific year
-    CBO_YEAR = 2023  # Year the CBO projections are for
+    # CBO projection targets - use time_period derived from dataset
     cbo_vars = [
         # Note: income_tax_positive matches CBO's receipts definition
         # where refundable credit payments in excess of liability are
@@ -326,7 +334,7 @@ def extract_national_targets():
         param_name = cbo_param_name_map.get(variable_name, variable_name)
         try:
             value = sim.tax_benefit_system.parameters(
-                CBO_YEAR
+                time_period
             ).calibration.gov.cbo._children[param_name]
             cbo_targets.append(
                 {
@@ -334,7 +342,7 @@ def extract_national_targets():
                     "value": float(value),
                     "source": "CBO Budget Projections",
                     "notes": f"CBO projection for {variable_name}",
-                    "year": CBO_YEAR,
+                    "year": time_period,
                 }
             )
         except (KeyError, AttributeError) as e:
@@ -343,11 +351,10 @@ def extract_national_targets():
                 f"{variable_name} (param: {param_name}): {e}"
             )
 
-    # Treasury/JCT targets (EITC) - get for a specific year
-    TREASURY_YEAR = 2023
+    # Treasury/JCT targets (EITC) - use time_period derived from dataset
     try:
         eitc_value = sim.tax_benefit_system.parameters.calibration.gov.treasury.tax_expenditures.eitc(
-            TREASURY_YEAR
+            time_period
         )
         treasury_targets = [
             {
@@ -355,7 +362,7 @@ def extract_national_targets():
                 "value": float(eitc_value),
                 "source": "Treasury/JCT Tax Expenditures",
                 "notes": "EITC tax expenditure",
-                "year": TREASURY_YEAR,
+                "year": time_period,
             }
         ]
     except (KeyError, AttributeError) as e:
@@ -368,6 +375,7 @@ def extract_national_targets():
         "conditional_count_targets": conditional_count_targets,
         "cbo_targets": cbo_targets,
         "treasury_targets": treasury_targets,
+        "time_period": time_period,
     }
 
 
@@ -707,10 +715,25 @@ def load_national_targets(
 
 def main():
     """Main ETL pipeline for national targets."""
+    parser = argparse.ArgumentParser(
+        description="ETL for national calibration targets"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The time_period for targets is derived from the dataset's "
+            "default_calculation_period. Default: %(default)s"
+        ),
+    )
+    args = parser.parse_args()
 
     # Extract
     print("Extracting national targets...")
-    raw_targets = extract_national_targets()
+    raw_targets = extract_national_targets(dataset=args.dataset)
+    time_period = raw_targets["time_period"]
+    print(f"Using time_period={time_period} for CBO/Treasury targets")
 
     # Transform
     print("Transforming targets...")
diff --git a/policyengine_us_data/db/etl_snap.py b/policyengine_us_data/db/etl_snap.py
index 48c1eb83..554f67ec 100644
--- a/policyengine_us_data/db/etl_snap.py
+++ b/policyengine_us_data/db/etl_snap.py
@@ -1,3 +1,4 @@
+import argparse
 import logging
 import requests
 import zipfile
@@ -10,6 +11,8 @@
 
 from policyengine_us_data.storage import STORAGE_FOLDER
 
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
+
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
@@ -149,9 +152,10 @@ def transform_survey_snap_data(raw_df):
         {"GEO_ID": "ucgid_str", "S2201_C03_001E": "snap_household_ct"}, axis=1
     )[
         ~df["GEO_ID"].isin(
-            [  # Puerto Rico's state and district
+            [  # Puerto Rico's state and district (118th and 119th Congress)
                 "0400000US72",
                 "5001800US7298",
+                "5001900US7298",
             ]
         )
     ]
@@ -363,7 +367,27 @@ def load_survey_snap_data(survey_df, year, snap_stratum_lookup):
 
 
 def main():
-    year = 2023
+    parser = argparse.ArgumentParser(
+        description="ETL for SNAP calibration targets"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The year for targets is derived from the dataset's "
+            "default_calculation_period. Default: %(default)s"
+        ),
+    )
+    args = parser.parse_args()
+
+    # Derive year from dataset
+    from policyengine_us import Microsimulation
+
+    print(f"Loading dataset: {args.dataset}")
+    sim = Microsimulation(dataset=args.dataset)
+    year = int(sim.default_calculation_period)
+    print(f"Derived year from dataset: {year}")
 
     # Extract ---------
     zip_file_admin = extract_administrative_snap_data()
diff --git a/policyengine_us_data/db/etl_state_income_tax.py b/policyengine_us_data/db/etl_state_income_tax.py
index df0f40a6..9da8d839 100644
--- a/policyengine_us_data/db/etl_state_income_tax.py
+++ b/policyengine_us_data/db/etl_state_income_tax.py
@@ -10,12 +10,15 @@
 Stratum Group ID: 7 (State Income Tax)
 """
 
+import argparse
 import logging
 import pandas as pd
 import numpy as np
 from sqlmodel import Session, create_engine, select
 
 from policyengine_us_data.storage import STORAGE_FOLDER
+
+DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
@@ -342,12 +345,32 @@ def load_state_income_tax_data(df: pd.DataFrame, year: int) -> dict:
 
 def main():
     """Run the full ETL pipeline for state income tax targets."""
+    parser = argparse.ArgumentParser(
+        description="ETL for state income tax calibration targets"
+    )
+    parser.add_argument(
+        "--dataset",
+        default=DEFAULT_DATASET,
+        help=(
+            "Source dataset (local path or HuggingFace URL). "
+            "The year for targets is derived from the dataset's "
+            "default_calculation_period. Default: %(default)s"
+        ),
+    )
+    args = parser.parse_args()
+
     logging.basicConfig(
         level=logging.INFO,
         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
     )
 
-    year = 2023
+    # Derive year from dataset
+    from policyengine_us import Microsimulation
+
+    logger.info(f"Loading dataset: {args.dataset}")
+    sim = Microsimulation(dataset=args.dataset)
+    year = int(sim.default_calculation_period)
+    logger.info(f"Derived year from dataset: {year}")
 
     logger.info(f"Extracting Census STC data for FY{year}...")
     raw_df = extract_state_income_tax_data(year)
diff --git a/policyengine_us_data/storage/calibration_targets/pull_soi_targets.py b/policyengine_us_data/storage/calibration_targets/pull_soi_targets.py
index 0b1f3dcb..c3f15919 100644
--- a/policyengine_us_data/storage/calibration_targets/pull_soi_targets.py
+++ b/policyengine_us_data/storage/calibration_targets/pull_soi_targets.py
@@ -41,11 +41,18 @@
 NON_VOTING_STATES = {"US", "AS", "GU", "MP", "PR", "VI", "OA"}
 NON_VOTING_GEO_IDS = {
     "0400000US72",  # Puerto Rico (state level)
+    # 118th Congress codes
     "5001800US7298",  # Puerto Rico
     "5001800US6098",  # American Samoa
     "5001800US6698",  # Guam
     "5001800US6998",  # Northern Mariana Islands
     "5001800US7898",  # U.S. Virgin Islands
+    # 119th Congress codes
+    "5001900US7298",  # Puerto Rico
+    "5001900US6098",  # American Samoa
+    "5001900US6698",  # Guam
+    "5001900US6998",  # Northern Mariana Islands
+    "5001900US7898",  # U.S. Virgin Islands
 }
 
 # after skipping the first 7 rows, the national SOI file has targets as row indices [COUNT_INDEX, AMOUNT_INDEX]
diff --git a/policyengine_us_data/utils/census.py b/policyengine_us_data/utils/census.py
index cb9d0b5d..c61cc166 100644
--- a/policyengine_us_data/utils/census.py
+++ b/policyengine_us_data/utils/census.py
@@ -123,11 +123,18 @@
 
 TERRITORY_UCGIDS = {
     "0400000US72",  # Puerto Rico (state level)
+    # 118th Congress codes
     "5001800US7298",  # Puerto Rico
     "5001800US6098",  # American Samoa
     "5001800US6698",  # Guam
     "5001800US6998",  # Northern Mariana Islands
     "5001800US7898",  # U.S. Virgin Islands
+    # 119th Congress codes
+    "5001900US7298",  # Puerto Rico
+    "5001900US6098",  # American Samoa
+    "5001900US6698",  # Guam
+    "5001900US6998",  # Northern Mariana Islands
+    "5001900US7898",  # U.S. Virgin Islands
 }
 
 
diff --git a/policyengine_us_data/utils/data_upload.py b/policyengine_us_data/utils/data_upload.py
index a94ca1a1..42cd8fee 100644
--- a/policyengine_us_data/utils/data_upload.py
+++ b/policyengine_us_data/utils/data_upload.py
@@ -362,6 +362,9 @@ def promote_staging_to_production_hf(
 
     Returns:
         Number of files promoted
+
+    Raises:
+        RuntimeError: If the commit was a no-op (HEAD unchanged)
     """
     token = os.environ.get("HUGGING_FACE_TOKEN")
     api = HfApi()
@@ -380,7 +383,13 @@ def promote_staging_to_production_hf(
         logging.warning("No files to promote.")
         return 0
 
-    hf_create_commit_with_retry(
+    head_before = api.repo_info(
+        repo_id=hf_repo_name,
+        repo_type=hf_repo_type,
+        token=token,
+    ).sha
+
+    result = hf_create_commit_with_retry(
         api=api,
         operations=operations,
         repo_id=hf_repo_name,
@@ -389,6 +398,12 @@ def promote_staging_to_production_hf(
         commit_message=f"Promote {len(files)} files from staging to production for version {version}",
     )
 
+    if result.oid == head_before:
+        raise RuntimeError(
+            f"Promote commit was a no-op: HEAD stayed at {head_before}. "
+            f"Staging files may be identical to production."
+        )
+
     logging.info(
         f"Promoted {len(files)} files from staging/ to production in one commit"
     )
@@ -412,6 +427,9 @@ def cleanup_staging_hf(
 
     Returns:
         Number of files deleted
+
+    Raises:
+        RuntimeError: If the cleanup commit was a no-op (HEAD unchanged)
     """
     token = os.environ.get("HUGGING_FACE_TOKEN")
     api = HfApi()
@@ -424,7 +442,13 @@ def cleanup_staging_hf(
     if not operations:
         return 0
 
-    hf_create_commit_with_retry(
+    head_before = api.repo_info(
+        repo_id=hf_repo_name,
+        repo_type=hf_repo_type,
+        token=token,
+    ).sha
+
+    result = hf_create_commit_with_retry(
         api=api,
         operations=operations,
         repo_id=hf_repo_name,
@@ -433,5 +457,11 @@ def cleanup_staging_hf(
         commit_message=f"Clean up staging after version {version} promotion",
     )
 
+    if result.oid == head_before:
+        raise RuntimeError(
+            f"Cleanup commit was a no-op: HEAD stayed at {head_before}. "
+            f"Staging files may not exist."
+        )
+
     logging.info(f"Cleaned up {len(files)} files from staging/")
     return len(files)
diff --git a/policyengine_us_data/utils/db.py b/policyengine_us_data/utils/db.py
index 6c7b1a4e..4de79c44 100644
--- a/policyengine_us_data/utils/db.py
+++ b/policyengine_us_data/utils/db.py
@@ -82,7 +82,10 @@ def parse_ucgid(ucgid_str: str) -> Dict:
     elif ucgid_str.startswith("0400000US"):
         state_fips = int(ucgid_str[9:])
         return {"type": "state", "state_fips": state_fips}
-    elif ucgid_str.startswith("5001800US"):
+    elif ucgid_str.startswith("5001800US") or ucgid_str.startswith(
+        "5001900US"
+    ):
+        # 5001800US = 118th Congress, 5001900US = 119th Congress
         state_and_district = ucgid_str[9:]
         state_fips = int(state_and_district[:2])
         district_number = int(state_and_district[2:])
diff --git a/policyengine_us_data/utils/loss.py b/policyengine_us_data/utils/loss.py
index f798c0dc..e9916641 100644
--- a/policyengine_us_data/utils/loss.py
+++ b/policyengine_us_data/utils/loss.py
@@ -220,26 +220,35 @@ def build_loss_matrix(dataset: type, time_period):
         targets_array.append(populations[year])
 
     # CBO projections
+    # Note: income_tax_positive matches CBO's receipts definition where
+    # refundable credit payments in excess of liability are classified as
+    # outlays, not negative receipts. See: https://www.cbo.gov/publication/43767
 
-    PROGRAMS = [
-        "income_tax",
+    CBO_PROGRAMS = [
+        "income_tax_positive",
         "snap",
         "social_security",
         "ssi",
         "unemployment_compensation",
     ]
 
-    for variable_name in PROGRAMS:
+    # Mapping from variable name to CBO parameter name (when different)
+    CBO_PARAM_NAME_MAP = {
+        "income_tax_positive": "income_tax",
+    }
+
+    for variable_name in CBO_PROGRAMS:
         label = f"nation/cbo/{variable_name}"
         loss_matrix[label] = sim.calculate(
             variable_name, map_to="household"
         ).values
         if any(loss_matrix[label].isna()):
             raise ValueError(f"Missing values for {label}")
+        param_name = CBO_PARAM_NAME_MAP.get(variable_name, variable_name)
         targets_array.append(
             sim.tax_benefit_system.parameters(
                 time_period
-            ).calibration.gov.cbo._children[variable_name]
+            ).calibration.gov.cbo._children[param_name]
         )
 
     # 1. Medicaid Spending
diff --git a/uv.lock b/uv.lock
index 4c2966f2..95f8747f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -637,6 +637,7 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f8/0a/a3871375c7b9727edaeeea994bfff7c63ff7804c9829c19309ba2e058807/greenlet-3.3.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:b01548f6e0b9e9784a2c99c5651e5dc89ffcbe870bc5fb2e5ef864e9cc6b5dcb", size = 276379, upload-time = "2025-12-04T14:23:30.498Z" },
     { url = "https://files.pythonhosted.org/packages/43/ab/7ebfe34dce8b87be0d11dae91acbf76f7b8246bf9d6b319c741f99fa59c6/greenlet-3.3.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:349345b770dc88f81506c6861d22a6ccd422207829d2c854ae2af8025af303e3", size = 597294, upload-time = "2025-12-04T14:50:06.847Z" },
     { url = "https://files.pythonhosted.org/packages/a4/39/f1c8da50024feecd0793dbd5e08f526809b8ab5609224a2da40aad3a7641/greenlet-3.3.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e8e18ed6995e9e2c0b4ed264d2cf89260ab3ac7e13555b8032b25a74c6d18655", size = 607742, upload-time = "2025-12-04T14:57:42.349Z" },
+    { url = "https://files.pythonhosted.org/packages/77/cb/43692bcd5f7a0da6ec0ec6d58ee7cddb606d055ce94a62ac9b1aa481e969/greenlet-3.3.0-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:c024b1e5696626890038e34f76140ed1daf858e37496d33f2af57f06189e70d7", size = 622297, upload-time = "2025-12-04T15:07:13.552Z" },
     { url = "https://files.pythonhosted.org/packages/75/b0/6bde0b1011a60782108c01de5913c588cf51a839174538d266de15e4bf4d/greenlet-3.3.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:047ab3df20ede6a57c35c14bf5200fcf04039d50f908270d3f9a7a82064f543b", size = 609885, upload-time = "2025-12-04T14:26:02.368Z" },
     { url = "https://files.pythonhosted.org/packages/49/0e/49b46ac39f931f59f987b7cd9f34bfec8ef81d2a1e6e00682f55be5de9f4/greenlet-3.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:2d9ad37fc657b1102ec880e637cccf20191581f75c64087a549e66c57e1ceb53", size = 1567424, upload-time = "2025-12-04T15:04:23.757Z" },
     { url = "https://files.pythonhosted.org/packages/05/f5/49a9ac2dff7f10091935def9165c90236d8f175afb27cbed38fb1d61ab6b/greenlet-3.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:83cd0e36932e0e7f36a64b732a6f60c2fc2df28c351bae79fbaf4f8092fe7614", size = 1636017, upload-time = "2025-12-04T14:27:29.688Z" },
@@ -644,6 +645,7 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/02/2f/28592176381b9ab2cafa12829ba7b472d177f3acc35d8fbcf3673d966fff/greenlet-3.3.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:a1e41a81c7e2825822f4e068c48cb2196002362619e2d70b148f20a831c00739", size = 275140, upload-time = "2025-12-04T14:23:01.282Z" },
     { url = "https://files.pythonhosted.org/packages/2c/80/fbe937bf81e9fca98c981fe499e59a3f45df2a04da0baa5c2be0dca0d329/greenlet-3.3.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9f515a47d02da4d30caaa85b69474cec77b7929b2e936ff7fb853d42f4bf8808", size = 599219, upload-time = "2025-12-04T14:50:08.309Z" },
     { url = "https://files.pythonhosted.org/packages/c2/ff/7c985128f0514271b8268476af89aee6866df5eec04ac17dcfbc676213df/greenlet-3.3.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7d2d9fd66bfadf230b385fdc90426fcd6eb64db54b40c495b72ac0feb5766c54", size = 610211, upload-time = "2025-12-04T14:57:43.968Z" },
+    { url = "https://files.pythonhosted.org/packages/79/07/c47a82d881319ec18a4510bb30463ed6891f2ad2c1901ed5ec23d3de351f/greenlet-3.3.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:30a6e28487a790417d036088b3bcb3f3ac7d8babaa7d0139edbaddebf3af9492", size = 624311, upload-time = "2025-12-04T15:07:14.697Z" },
     { url = "https://files.pythonhosted.org/packages/fd/8e/424b8c6e78bd9837d14ff7df01a9829fc883ba2ab4ea787d4f848435f23f/greenlet-3.3.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:087ea5e004437321508a8d6f20efc4cfec5e3c30118e1417ea96ed1d93950527", size = 612833, upload-time = "2025-12-04T14:26:03.669Z" },
     { url = "https://files.pythonhosted.org/packages/b5/ba/56699ff9b7c76ca12f1cdc27a886d0f81f2189c3455ff9f65246780f713d/greenlet-3.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ab97cf74045343f6c60a39913fa59710e4bd26a536ce7ab2397adf8b27e67c39", size = 1567256, upload-time = "2025-12-04T15:04:25.276Z" },
     { url = "https://files.pythonhosted.org/packages/1e/37/f31136132967982d698c71a281a8901daf1a8fbab935dce7c0cf15f942cc/greenlet-3.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5375d2e23184629112ca1ea89a53389dddbffcf417dad40125713d88eb5f96e8", size = 1636483, upload-time = "2025-12-04T14:27:30.804Z" },
@@ -1842,7 +1844,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-us"
-version = "1.524.1"
+version = "1.550.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "microdf-python" },
@@ -1850,9 +1852,9 @@ dependencies = [
     { name = "policyengine-core" },
     { name = "tqdm" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/2b/f0/623a10a84d501039b100808f3f42020ae68c5a793ff2865c58a034be1b32/policyengine_us-1.524.1.tar.gz", hash = "sha256:329ab166681264fcacbe4549afa023cf96bda2348d48bfc385e8328afc811ec8", size = 8503673, upload-time = "2026-01-26T20:47:54.189Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/03/a7/031dfe91081446203cf90203ca3305b09a93d495852df18a5177d3784bce/policyengine_us-1.550.1.tar.gz", hash = "sha256:7c0900d5f09ceadcc2047a2f7a4f84e23e30f461653a358ed49c0eb1f1337a66", size = 8606705, upload-time = "2026-02-04T19:52:56.341Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/08/c53fd0555b2fa95cb81994ae20f4fb9df1d35b14efad26f911ff6d0ead75/policyengine_us-1.524.1-py3-none-any.whl", hash = "sha256:e940b977090a2aea782724668d798ddf667b1b1bda7036e93f3caeaf853ea436", size = 7398760, upload-time = "2026-01-26T20:47:51.851Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/52/cceeb4ee203addcf049c7443fa53455a66962b650e09e3f82e36d65b427e/policyengine_us-1.550.1-py3-none-any.whl", hash = "sha256:9e1fd0c709502ff69b6051ecd7ed5fea49bd01629790867168651f4a117cb703", size = 7600766, upload-time = "2026-02-04T19:52:53.92Z" },
 ]
 
 [[package]]