diff --git a/HARK/distribution.py b/HARK/distribution.py
index 22314c9cb..7c3d39321 100644
--- a/HARK/distribution.py
+++ b/HARK/distribution.py
@@ -1181,7 +1181,7 @@ def func_wrapper(x: np.ndarray, *args: Any) -> np.ndarray:
 
         if len(kwargs):
             f_query = func(self.dataset, **kwargs)
-            ldd = DiscreteDistributionLabeled.from_dataset(f_query, self.pmv)
+            ldd = DiscreteDistributionLabeled.from_dataset(f_query, self.probability)
 
             return ldd
 
diff --git a/HARK/tests/test_distribution.py b/HARK/tests/test_distribution.py
index ced58fbf5..ec685eaa3 100644
--- a/HARK/tests/test_distribution.py
+++ b/HARK/tests/test_distribution.py
@@ -1,6 +1,7 @@
 import unittest
 
 import numpy as np
+import xarray as xr
 
 from HARK.distribution import (
     Bernoulli,
@@ -603,3 +604,41 @@ def test_combine_labeled_dist(self):
                 np.concatenate([de.expected(), abc.expected()]),
             )
         )
+
+
+class labeled_transition_tests(unittest.TestCase):
+    def setUp(self) -> None:
+        return super().setUp()
+
+    def test_expectation_transformation(self):
+        # Create a basic labeled distribution
+        base_dist = DiscreteDistributionLabeled(
+            pmv=np.array([0.5, 0.5]),
+            atoms=np.array([[1.0, 2.0], [3.0, 4.0]]),
+            var_names=["a", "b"],
+        )
+
+        # Define a transition function
+        def transition(shocks, state):
+            state_new = {}
+            state_new["m"] = state["m"] * shocks["a"]
+            state_new["n"] = state["n"] * shocks["b"]
+            return state_new
+
+        m = xr.DataArray(np.linspace(0, 10, 11), name="m", dims=("grid",))
+        n = xr.DataArray(np.linspace(0, -10, 11), name="n", dims=("grid",))
+        state_grid = xr.Dataset({"m": m, "n": n})
+
+        # Evaluate labeled transformation
+
+        # Direct expectation
+        exp1 = base_dist.expected(transition, state=state_grid)
+        # Expectation after transformation
+        new_state_dstn = base_dist.dist_of_func(transition, state=state_grid)
+        # TODO: needs a cluncky identity function with an extra argument because
+        # DDL.expected() behavior is very different with and without kwargs.
+        # Fix!
+        exp2 = new_state_dstn.expected(lambda x, unused: x, unused=0)
+
+        assert np.all(exp1["m"] == exp2["m"]).item()
+        assert np.all(exp1["n"] == exp2["n"]).item()