diff --git a/policyengine_us_data/datasets/__init__.py b/policyengine_us_data/datasets/__init__.py
index ffde8e4..8746183 100644
--- a/policyengine_us_data/datasets/__init__.py
+++ b/policyengine_us_data/datasets/__init__.py
@@ -18,4 +18,11 @@
 from .puf import PUF_2015, PUF_2021, PUF_2024, IRS_PUF_2015
 from .acs import ACS_2022
 
-DATASETS = [CPS_2022, PUF_2021, CPS_2024, EnhancedCPS_2024, ACS_2022]
+DATASETS = [
+    CPS_2022,
+    PUF_2021,
+    CPS_2024,
+    EnhancedCPS_2024,
+    ACS_2022,
+    Pooled_3_Year_CPS_2023,
+]
diff --git a/policyengine_us_data/utils/loss.py b/policyengine_us_data/utils/loss.py
index ecdb2f7..fec5af9 100644
--- a/policyengine_us_data/utils/loss.py
+++ b/policyengine_us_data/utils/loss.py
@@ -226,6 +226,19 @@ def build_loss_matrix(dataset: type, time_period):
             raise ValueError(f"Missing values for {label}")
         targets_array.append(target)
 
+    # Negative household market income total rough estimate from the IRS SOI PUF
+
+    market_income = sim.calculate("household_market_income").values
+    loss_matrix["irs/negative_household_market_income_total"] = (
+        market_income * (market_income < 0)
+    )
+    targets_array.append(-138e9)
+
+    loss_matrix["irs/negative_household_market_income_count"] = (
+        market_income < 0
+    )
+    targets_array.append(3e6)
+
     # Healthcare spending by age
 
     healthcare = pd.read_csv(STORAGE_FOLDER / "healthcare_spending.csv")