diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29b..74643c86 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    added:
+    - Add state-level TANF takeup rates and assign takeup during CPS microdata construction.
diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py
index 4a5fa36d..6ffa2808 100644
--- a/policyengine_us_data/datasets/cps/cps.py
+++ b/policyengine_us_data/datasets/cps/cps.py
@@ -207,6 +207,7 @@ def add_takeup(self):
     snap_rate = load_take_up_rate("snap", self.time_period)
     aca_rate = load_take_up_rate("aca", self.time_period)
     medicaid_rates_by_state = load_take_up_rate("medicaid", self.time_period)
+    tanf_rates_by_state = load_take_up_rate("tanf", self.time_period)
     head_start_rate = load_take_up_rate("head_start", self.time_period)
     early_head_start_rate = load_take_up_rate(
         "early_head_start", self.time_period
@@ -232,15 +233,35 @@ def add_takeup(self):
     rng = seeded_rng("takes_up_snap_if_eligible")
     data["takes_up_snap_if_eligible"] = rng.random(n_spm_units) < snap_rate
 
+    # Shared state lookup used by TANF and Medicaid
+    state_codes = baseline.calculate("state_code_str").values
+    hh_ids = data["household_id"]
+    person_hh_ids = data["person_household_id"]
+    hh_to_state = dict(zip(hh_ids, state_codes))
+
+    # TANF: state-specific rates at SPM unit level
+    # Map each SPM unit to a state via its first member's household
+    spm_to_state = {}
+    for person_idx, spm_id in enumerate(data["person_spm_unit_id"]):
+        if spm_id not in spm_to_state:
+            hh_id = person_hh_ids[person_idx]
+            spm_to_state[spm_id] = hh_to_state.get(hh_id, "CA")
+    tanf_rate_by_spm = np.array(
+        [
+            tanf_rates_by_state.get(spm_to_state[spm_id], 0.21)
+            for spm_id in data["spm_unit_id"]
+        ]
+    )
+    rng = seeded_rng("takes_up_tanf_if_eligible")
+    data["takes_up_tanf_if_eligible"] = (
+        rng.random(n_spm_units) < tanf_rate_by_spm
+    )
+
     # ACA
     rng = seeded_rng("takes_up_aca_if_eligible")
     data["takes_up_aca_if_eligible"] = rng.random(n_tax_units) < aca_rate
 
     # Medicaid: state-specific rates
-    state_codes = baseline.calculate("state_code_str").values
-    hh_ids = data["household_id"]
-    person_hh_ids = data["person_household_id"]
-    hh_to_state = dict(zip(hh_ids, state_codes))
     person_states = np.array(
         [hh_to_state.get(hh_id, "CA") for hh_id in person_hh_ids]
     )
diff --git a/policyengine_us_data/parameters/__init__.py b/policyengine_us_data/parameters/__init__.py
index 2fcddb5a..f49a5ba7 100644
--- a/policyengine_us_data/parameters/__init__.py
+++ b/policyengine_us_data/parameters/__init__.py
@@ -19,7 +19,7 @@ def load_take_up_rate(variable_name: str, year: int = 2018):
         year: Year for which to get the rate
 
     Returns:
-        float, dict (EITC rates_by_children), or dict (Medicaid
+        float, dict (EITC rates_by_children), or dict (Medicaid/TANF
         rates_by_state)
     """
     yaml_path = PARAMETERS_DIR / "take_up" / f"{variable_name}.yaml"
@@ -31,7 +31,7 @@ def load_take_up_rate(variable_name: str, year: int = 2018):
     if "rates_by_children" in data:
         return data["rates_by_children"]
 
-    # Medicaid: state-specific rates
+    # State-specific rates (Medicaid, TANF, etc.)
     if "rates_by_state" in data:
         return data["rates_by_state"]
 
diff --git a/policyengine_us_data/parameters/take_up/tanf.yaml b/policyengine_us_data/parameters/take_up/tanf.yaml
new file mode 100644
index 00000000..bfa37a2d
--- /dev/null
+++ b/policyengine_us_data/parameters/take_up/tanf.yaml
@@ -0,0 +1,71 @@
+description: >-
+  Percentage of eligible TANF families who receive TANF cash assistance.
+  Priors derived from ACF FY2023 TANF caseload data divided by Census
+  ACS 2023 families in poverty, scaled up ~2x to approximate takeup
+  among eligible families (since eligible families are roughly half of
+  families in poverty due to asset tests, work requirements, and time
+  limits). These priors will be adjusted during calibration against the
+  $9B national cash assistance target.
+metadata:
+  label: TANF takeup rate
+  unit: /1
+  period: year
+  breakdown:
+    - state_code
+  reference:
+    - title: ACF FY2023 TANF Caseload Data
+      href: https://acf.gov/ofa/data/tanf-caseload-data-2023
+    - title: Census Bureau 2023 ACS 1-Year Estimates (Table S1702)
+      href: https://data.census.gov/table/ACSST1Y2023.S1702
+rates_by_state:
+  AK: 0.18
+  AL: 0.10
+  AR: 0.03
+  AZ: 0.08
+  CA: 0.91
+  CO: 0.33
+  CT: 0.21
+  DC: 0.54
+  DE: 0.37
+  FL: 0.21
+  GA: 0.05
+  HI: 0.36
+  IA: 0.21
+  ID: 0.12
+  IL: 0.11
+  IN: 0.08
+  KS: 0.14
+  KY: 0.22
+  LA: 0.08
+  MA: 0.83
+  MD: 0.45
+  ME: 0.38
+  MI: 0.11
+  MN: 0.46
+  MO: 0.10
+  MS: 0.04
+  MT: 0.23
+  NC: 0.10
+  ND: 0.14
+  NE: 0.24
+  NH: 0.45
+  NJ: 0.19
+  NM: 0.32
+  NV: 0.24
+  NY: 0.52
+  OH: 0.39
+  OK: 0.09
+  OR: 0.62
+  PA: 0.29
+  RI: 0.42
+  SC: 0.12
+  SD: 0.40
+  TN: 0.22
+  TX: 0.04
+  UT: 0.13
+  VA: 0.36
+  VT: 0.43
+  WA: 0.66
+  WI: 0.37
+  WV: 0.26
+  WY: 0.15