diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py
index 9771b43f..ca6759b2 100644
--- a/policyengine_us_data/datasets/cps/cps.py
+++ b/policyengine_us_data/datasets/cps/cps.py
@@ -231,6 +231,7 @@ def add_personal_variables(cps: h5py.File, person: DataFrame) -> None:
         np.random.randint(80, 85, len(person)),
         person.A_AGE,
     )
+
     # A_SEX is 1 -> male, 2 -> female.
     cps["is_female"] = person.A_SEX == 2
     # "Is...blind or does...have serious difficulty seeing even when Wearing
@@ -241,6 +242,29 @@ def add_personal_variables(cps: h5py.File, person: DataFrame) -> None:
     ]
     cps["is_disabled"] = (person[DISABILITY_FLAGS] == 1).any(axis=1)
 
+    def _assign_some_newborns_to_pregnancy(
+        age: pd.Series, person: pd.DataFrame
+    ) -> pd.Series:
+        """Takes an array of ages, returns the new age array with the given percentage of newborns assigned a negative age (in pregnancy)."""
+        age = np.where(
+            person.A_AGE == 0,
+            np.where(
+                np.random.randint(
+                    0, 2, len(person)
+                ),  # Random number of 0 or 1
+                # If 1 is flipped, select a random number between -0.75 and 0
+                # This will represent the pregnany month
+                # At -0.75 the pregnancy month is 0 and at -0.0001 the pregnancy month is 9
+                np.random.uniform(-0.75, 0, len(person)),
+                # If 0 is flipped, the child is a newborn at the age of 0 to 1
+                np.random.uniform(0, 1, len(person)),
+            ),
+            person.A_AGE,
+        )
+        return age
+
+    cps["age"] = _assign_some_newborns_to_pregnancy(cps["age"], person)
+
     def children_per_parent(col: str) -> pd.DataFrame:
         """Calculate number of children in the household using parental
             pointers.
diff --git a/policyengine_us_data/tests/test_datasets/test_enhanced_cps.py b/policyengine_us_data/tests/test_datasets/test_enhanced_cps.py
index 4d9c7f9b..e6bf595c 100644
--- a/policyengine_us_data/tests/test_datasets/test_enhanced_cps.py
+++ b/policyengine_us_data/tests/test_datasets/test_enhanced_cps.py
@@ -37,3 +37,20 @@ def test_ecps_has_mortgage_interest():
 
     assert sim.calculate("deductible_mortgage_interest").sum() > 1
     assert sim.calculate("deductible_interest_expense").sum() > 1
+
+
+def test_newborns_and_pregnancies():
+    from policyengine_us_data.datasets.cps import EnhancedCPS_2024
+    from policyengine_us import Microsimulation
+
+    sim = Microsimulation(dataset=EnhancedCPS_2024)
+
+    # Test for unborn children (age < 0)
+    unborn = sim.calculate("age") < 0
+    unborn_count = unborn.sum()
+    assert unborn_count > 0
+
+    # Test for newborns (0 <= age < 1)
+    newborns = (sim.calculate("age") >= 0) & (sim.calculate("age") < 1)
+    newborn_count = newborns.sum()
+    assert newborn_count > 0
diff --git a/policyengine_us_data/utils/loss.py b/policyengine_us_data/utils/loss.py
index a01b16a4..b743dc46 100644
--- a/policyengine_us_data/utils/loss.py
+++ b/policyengine_us_data/utils/loss.py
@@ -340,6 +340,19 @@ def build_loss_matrix(dataset: type, time_period):
         )
         targets_array.append(row["population_under_5"])
 
+    # Population by number of newborns and pregancies
+
+    age = sim.calculate("age").values
+    infants = (age >= 0) & (age < 1)
+    label = "census/infants"
+    loss_matrix[label] = sim.map_result(infants, "person", "household")
+    targets_array.append(3_491_679)
+
+    pregnancies = (age >= -0.75) & (age < 0)
+    label = "census/pregnancies"
+    loss_matrix[label] = sim.map_result(pregnancies, "person", "household")
+    targets_array.append(2_618_759)
+
     if any(loss_matrix.isna().sum() > 0):
         raise ValueError("Some targets are missing from the loss matrix")