diff --git a/data/data-pipeline/data_pipeline/tests/score/fixtures.py b/data/data-pipeline/data_pipeline/tests/score/fixtures.py
index 2b1c7fc68..64d80bfad 100644
--- a/data/data-pipeline/data_pipeline/tests/score/fixtures.py
+++ b/data/data-pipeline/data_pipeline/tests/score/fixtures.py
@@ -205,3 +205,15 @@ def hrs_df():
         dtype={GEOID_TRACT_FIELD_NAME: "string"},
         low_memory=False,
     )
+
+
+@pytest.fixture()
+def national_tract_df():
+    national_tract_csv = constants.DATA_CENSUS_CSV_FILE_PATH
+    return pd.read_csv(
+        national_tract_csv,
+        names=[GEOID_TRACT_FIELD_NAME],
+        dtype={GEOID_TRACT_FIELD_NAME: "string"},
+        low_memory=False,
+        header=None,
+    )
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_output.py b/data/data-pipeline/data_pipeline/tests/score/test_output.py
index b79b5c711..6f73553eb 100644
--- a/data/data-pipeline/data_pipeline/tests/score/test_output.py
+++ b/data/data-pipeline/data_pipeline/tests/score/test_output.py
@@ -27,6 +27,7 @@
     census_decennial_df,
     census_2010_df,
     hrs_df,
+    national_tract_df,
 )
 
 
@@ -255,7 +256,7 @@ def test_data_sources(
     }
 
     for data_source_name, data_source in data_sources.items():
-        final = "final"
+        final = "final_"
         df: pd.DataFrame = final_score_df.merge(
             data_source,
             on=GEOID_TRACT_FIELD_NAME,
@@ -264,21 +265,21 @@ def test_data_sources(
             how="left",
         )
 
-        # Make sure we have NAs for any tracts in the final data that aren't
-        # covered in the  final data
-        assert np.all(df[df.MERGE == "left_only"][final_columns].isna())
-        df = df[df.MERGE == "both"]
-
         # Make our lists of columns for later comparison
         core_cols = data_source.columns.intersection(
             final_score_df.columns
         ).drop(GEOID_TRACT_FIELD_NAME)
         data_source_columns = [f"{col}_{data_source_name}" for col in core_cols]
-        final_columns = [f"{col}_{final}" for col in core_cols]
+        final_columns = [f"{col}{final}" for col in core_cols]
         assert (
             final_columns
         ), f"No columns from data source show up in final score in source {data_source_name}"
 
+        # Make sure we have NAs for any tracts in the final data that aren't
+        # covered in the  final data
+        assert np.all(df[df.MERGE == "left_only"][final_columns].isna())
+        df = df[df.MERGE == "both"]
+
         # Compare every column for equality, using close equality for numerics and
         # `equals` equality for non-numeric columns
         for final_column, data_source_column in zip(
@@ -302,3 +303,15 @@ def test_data_sources(
                     df[data_source_column],
                     equal_nan=True,
                 ), error_message
+
+
+def test_output_tracts(final_score_df, national_tract_df):
+    df = final_score_df.merge(
+        national_tract_df,
+        on=GEOID_TRACT_FIELD_NAME,
+        how="outer",
+        indicator="MERGE",
+    )
+    counts = df.value_counts("MERGE")
+    assert counts.loc["left_only"] == 0
+    assert counts.loc["right_only"] == 0