diff --git a/scripts/world_bank/wdi/worldbank.py b/scripts/world_bank/wdi/worldbank.py
index 126e4e281e..dcb5d4757f 100644
--- a/scripts/world_bank/wdi/worldbank.py
+++ b/scripts/world_bank/wdi/worldbank.py
@@ -255,9 +255,11 @@ def read_worldbank(iso3166alpha3, mode):
             if file.startswith("API"):
                 file_to_open = file
                 break
-        assert file_to_open is not None, \
-            "Failed to find data for" + iso3166alpha3
-
+        if file_to_open is None:
+            logging.warning(
+                'Failed to find data for %s in the downloaded ZIP. Skipping.',
+                iso3166alpha3)
+            return None
         df = None
         # Captures any text contained in double quotatations.
         line_match = re.compile(r"\"([^\"]*)\"")
@@ -415,15 +417,21 @@ def download_indicator_data(worldbank_countries, indicator_codes, mode):
     for index, country_code in enumerate(worldbank_countries['ISO3166Alpha3']):
         country_df = read_worldbank(country_code, mode)
 
+        if country_df is None:
+            continue
+
         # Remove unneccessary indicators.
         country_df = country_df[country_df['IndicatorCode'].isin(
             indicators_to_keep)]
-
         # Map country codes to ISO.
         country_df['ISO3166Alpha3'] = country_code
-
         # Add new row to main datframe.
         country_df_list.append(country_df)
+    # 3. Handle the empty list case OUTSIDE the loop
+    if not country_df_list:
+        logging.error("No data was downloaded for any country.")
+        # Return empty DF with expected columns to satisfy the rest of the pipeline
+        return pd.DataFrame(columns=['StatisticalVariable', 'Year', 'Value'])
 
     worldbank_dataframe = pd.concat(country_df_list)
     # Map indicator codes to unique Statistical Variable.