Fix EITC calibration (#98)

* Add dropout

* Add dropout and EITC calibration

* Format

* Add install catch

* Change download folder

* Flip order of US install and bump US

* Add EITC targets

* Update data links

* Fix EITC returns are incorrectly calibrated #97

* Lower dropout rate to 5%

* Update validation

* Update data releases

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    fixed:
+    - EITC calibration.

policyengine_us_data/datasets/acs/acs.py

@@ -111,7 +111,7 @@ class ACS_2022(ACS):
     time_period = 2022
     file_path = STORAGE_FOLDER / "acs_2022.h5"
     census_acs = CensusACS_2022
-    url = "release://PolicyEngine/policyengine-us-data/1.9.0/acs_2022.h5"
+    url = "release://PolicyEngine/policyengine-us-data/1.10.0/acs_2022.h5"
 
 
 if __name__ == "__main__":

policyengine_us_data/datasets/cps/cps.py

@@ -54,6 +54,8 @@ def generate(self):
         raw_data.close()
         self.save_dataset(cps)
 
+        add_takeup(self)
+
 
 def add_rent(self, cps: h5py.File, person: DataFrame, household: DataFrame):
     cps["tenure_type"] = household.H_TENURE.map(
@@ -114,6 +116,30 @@ def add_rent(self, cps: h5py.File, person: DataFrame, household: DataFrame):
     cps["real_estate_taxes"][mask] = imputed_values["real_estate_taxes"]
 
 
+def add_takeup(self):
+    data = self.load_dataset()
+
+    from policyengine_us import system, Microsimulation
+
+    baseline = Microsimulation(dataset=self)
+    parameters = baseline.tax_benefit_system.parameters(self.time_period)
+    generator = np.random.default_rng(seed=100)
+
+    snap_takeup_rate = parameters.gov.usda.snap.takeup_rate
+    data["takes_up_snap_if_eligible"] = (
+        generator.random(len(data["spm_unit_id"])) < snap_takeup_rate
+    )
+
+    eitc_takeup_rates = parameters.gov.irs.credits.eitc.takeup
+    eitc_child_count = baseline.calculate("eitc_child_count").values
+    eitc_takeup_rate = eitc_takeup_rates.calc(eitc_child_count)
+    data["takes_up_eitc"] = (
+        generator.random(len(data["tax_unit_id"])) < eitc_takeup_rate
+    )
+
+    self.save_dataset(data)
+
+
 def uprate_cps_data(data, from_period, to_period):
     uprating = create_policyengine_uprating_factors_table()
     for variable in uprating.index.unique():
@@ -622,7 +648,7 @@ class CPS_2024(CPS):
     label = "CPS 2024 (2022-based)"
     file_path = STORAGE_FOLDER / "cps_2024.h5"
     time_period = 2024
-    url = "release://policyengine/policyengine-us-data/1.9.0/cps_2024.h5"
+    url = "release://policyengine/policyengine-us-data/1.10.0/cps_2024.h5"
 
 
 class PooledCPS(Dataset):
@@ -681,7 +707,7 @@ class Pooled_3_Year_CPS_2023(PooledCPS):
         CPS_2023,
     ]
     time_period = 2023
-    url = "release://PolicyEngine/policyengine-us-data/1.9.0/pooled_3_year_cps_2023.h5"
+    url = "release://PolicyEngine/policyengine-us-data/1.10.0/pooled_3_year_cps_2023.h5"
 
 
 if __name__ == "__main__":

policyengine_us_data/datasets/cps/enhanced_cps.py

@@ -25,7 +25,7 @@ def reweight(
     original_weights,
     loss_matrix,
     targets_array,
-    dropout_rate=0.1,
+    dropout_rate=0.05,
 ):
     target_names = np.array(loss_matrix.columns)
     loss_matrix = torch.tensor(loss_matrix.values, dtype=torch.float32)
@@ -189,7 +189,7 @@ class EnhancedCPS_2024(EnhancedCPS):
     name = "enhanced_cps_2024"
     label = "Enhanced CPS 2024"
     file_path = STORAGE_FOLDER / "enhanced_cps_2024.h5"
-    url = "release://policyengine/policyengine-us-data/1.9.0/enhanced_cps_2024.h5"
+    url = "release://policyengine/policyengine-us-data/1.10.0/enhanced_cps_2024.h5"
 
 
 if __name__ == "__main__":

policyengine_us_data/utils/loss.py

@@ -208,8 +208,16 @@ def build_loss_matrix(dataset: type, time_period):
         )
         eitc_eligible_children = sim.calculate("eitc_child_count").values
         eitc = sim.calculate("eitc").values
+        if row["count_children"] < 2:
+            meets_child_criteria = (
+                eitc_eligible_children == row["count_children"]
+            )
+        else:
+            meets_child_criteria = (
+                eitc_eligible_children >= row["count_children"]
+            )
         loss_matrix[returns_label] = sim.map_result(
-            (eitc > 0) * (eitc_eligible_children == row["count_children"]),
+            (eitc > 0) * meets_child_criteria,
             "tax_unit",
             "household",
         )
@@ -219,7 +227,7 @@ def build_loss_matrix(dataset: type, time_period):
             f"irs/eitc/spending/count_children_{row['count_children']}"
         )
         loss_matrix[spending_label] = sim.map_result(
-            eitc * (eitc_eligible_children == row["count_children"]),
+            eitc * meets_child_criteria,
             "tax_unit",
             "household",
         )