diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py
index 692ac243..00d3fe2a 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py
@@ -10,11 +10,11 @@ from data_pipeline.utils import get_module_logger
 logger = get_module_logger(__name__)
 
 
-def _prepare_dataframe_for_imputation(
+def _get_impute_tract_list(
     columns_to_impute: list,
     geo_df: gpd.GeoDataFrame,
     geoid_field: str = "GEOID10_TRACT",
-):
+) -> list:
 
     # generate a list of tracts for which at least one of the imputation
     # columns is null
@@ -26,30 +26,35 @@ def _prepare_dataframe_for_imputation(
     logger.info(f"Imputing values for {len(tract_list)} unique tracts.")
     assert len(tract_list) > 0, "Error: No missing values to impute"
 
-    return tract_list, geo_df
+    return tract_list
 
 
-def _get_state_and_county_fills(df, tract_list, impute_var_pair_list):
+def _get_state_and_county_fills(
+    df: pd.DataFrame, tract_list: list, impute_var_pair_list: list
+) -> pd.DataFrame:
+    counties = df[GEOID_TRACT_FIELD].str[:5]  # county fips is 5 digits
+    states = df[GEOID_TRACT_FIELD].str[:2]  # state fips is 2 digits
     # When there is no neighbor average, we take the county-level average or state-level averages
     for impute_var_pair in impute_var_pair_list:
-        # Fill missings with county means
-        df[impute_var_pair.imputed_field_name] = np.where(
-            (df[impute_var_pair.imputed_field_name].isna())
-            & (df[GEOID_TRACT_FIELD].isin(tract_list)),
-            df.groupby(df[GEOID_TRACT_FIELD].str[:5])[
-                impute_var_pair.raw_field_name
-            ].transform(np.mean),
-            df[impute_var_pair.imputed_field_name],
+        # Get a column of county means or state means when county means are not available
+        county_means = df.groupby(counties)[
+            impute_var_pair.raw_field_name
+        ].transform(np.mean)
+        state_means = df.groupby(states)[
+            impute_var_pair.raw_field_name
+        ].transform(np.mean)
+        fill_means = county_means.fillna(state_means)
+
+        # Identify where these must be imputed
+        impute_tracts = (df[impute_var_pair.imputed_field_name].isna()) & (
+            df[GEOID_TRACT_FIELD].isin(tract_list)
         )
-        # Fill the remaining missings with state means
+
+        # And then impute while preserving null character elsewhere
         df[impute_var_pair.imputed_field_name] = np.where(
-            (df[impute_var_pair.imputed_field_name].isna())
-            & (df[GEOID_TRACT_FIELD].isin(tract_list)),
-            df.groupby(df[GEOID_TRACT_FIELD].str[:2])[
-                impute_var_pair.raw_field_name
-            ].transform(np.mean),
-            df[impute_var_pair.imputed_field_name],
+            impute_tracts, fill_means, df[impute_var_pair.imputed_field_name]
         )
+
     return df
 
 
@@ -82,7 +87,7 @@ def calculate_income_measures(
         rename_dict[impute_var.raw_field_name] = impute_var.imputed_field_name
 
     # Determine where to impute variables and fill a column with nulls
-    tract_list, geo_df = _prepare_dataframe_for_imputation(
+    tract_list = _get_impute_tract_list(
         columns_to_impute=raw_fields,
         geo_df=geo_df,
         geoid_field=geoid_field,