feat(scoring): Score v6 — World Bank global economic data for non-EU countries

Non-EU countries (AR, MX, AE, AU, etc.) previously got NULL for
median_income_pps and pli_construction, falling back to EU-calibrated
defaults (15K PPS, PLI=100) that produced wrong scores.

New World Bank WDI extractor fetches GNI per capita PPP and price level
ratio for 215 countries. dim_countries uses Germany as calibration anchor
to scale WB values into the Eurostat range (dynamic ratio, self-corrects
as both sources update). EU countries keep exact Eurostat values.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

extract/padelnomics_extract/pyproject.toml

@@ -22,6 +22,7 @@ extract-census-usa-income = "padelnomics_extract.census_usa_income:main"
 extract-ons-uk = "padelnomics_extract.ons_uk:main"
 extract-geonames = "padelnomics_extract.geonames:main"
 extract-gisco = "padelnomics_extract.gisco:main"
+extract-worldbank = "padelnomics_extract.worldbank:main"
 
 [build-system]
 requires = ["hatchling"]

extract/padelnomics_extract/src/padelnomics_extract/all.py

@@ -7,7 +7,7 @@ A graphlib.TopologicalSorter schedules them: tasks with no unmet dependencies
 run immediately in parallel; each completion may unlock new tasks.
 
 Current dependency graph:
-  - All 9 non-availability extractors have no dependencies (run in parallel)
+  - All 10 non-availability extractors have no dependencies (run in parallel)
   - playtomic_availability depends on playtomic_tenants (starts as soon as
     tenants finishes, even if other extractors are still running)
 """
@@ -38,6 +38,8 @@ from .playtomic_availability import EXTRACTOR_NAME as AVAILABILITY_NAME
 from .playtomic_availability import extract as extract_availability
 from .playtomic_tenants import EXTRACTOR_NAME as TENANTS_NAME
 from .playtomic_tenants import extract as extract_tenants
+from .worldbank import EXTRACTOR_NAME as WORLDBANK_NAME
+from .worldbank import extract as extract_worldbank
 
 logger = setup_logging("padelnomics.extract")
 
@@ -54,6 +56,7 @@ EXTRACTORS: dict[str, tuple] = {
     GEONAMES_NAME:             (extract_geonames, []),
     GISCO_NAME:                (extract_gisco, []),
     TENANTS_NAME:              (extract_tenants, []),
+    WORLDBANK_NAME:            (extract_worldbank, []),
     AVAILABILITY_NAME:         (extract_availability, [TENANTS_NAME]),
 }
 

extract/padelnomics_extract/src/padelnomics_extract/worldbank.py

@@ -0,0 +1,153 @@
+"""World Bank WDI extractor — GNI per capita PPP and price level ratio.
+
+Fetches two indicators (one API call each, no key required):
+  - NY.GNP.PCAP.PP.CD  — GNI per capita, PPP (international $)
+  - PA.NUS.PPPC.RF      — Price level ratio (PPP conversion factor / exchange rate)
+
+These provide global fallbacks behind Eurostat for dim_countries.median_income_pps
+and dim_countries.pli_construction (see dim_countries.sql for calibration logic).
+
+API: World Bank API v2 — https://api.worldbank.org/v2/
+No API key required. No env vars.
+
+Landing: {LANDING_DIR}/worldbank/{year}/{month}/wdi_indicators.json.gz
+Output:  {"rows": [{"country_code": "DE", "indicator": "NY.GNP.PCAP.PP.CD",
+                    "ref_year": 2023, "value": 74200.0}, ...], "count": N}
+"""
+
+import json
+import sqlite3
+from pathlib import Path
+
+import niquests
+
+from ._shared import HTTP_TIMEOUT_SECONDS, run_extractor, setup_logging
+from .utils import get_last_cursor, landing_path, write_gzip_atomic
+
+logger = setup_logging("padelnomics.extract.worldbank")
+
+EXTRACTOR_NAME = "worldbank"
+
+INDICATORS = ["NY.GNP.PCAP.PP.CD", "PA.NUS.PPPC.RF"]
+# 6 years of data — we take the latest non-null per country in staging
+DATE_RANGE = "2019:2025"
+MAX_PER_PAGE = 5000
+MAX_PAGES = 3
+
+WDI_BASE_URL = "https://api.worldbank.org/v2/country/all/indicator"
+
+# WB aggregate codes that look like real 2-letter country codes.
+# These are regional/income-group aggregates, not actual countries.
+_WB_AGGREGATE_CODES = frozenset({
+    "EU", "OE",
+    "XC", "XD", "XE", "XF", "XG", "XH", "XI", "XJ", "XL", "XM",
+    "XN", "XO", "XP", "XQ", "XR", "XS", "XT", "XU", "XV", "XY",
+    "ZF", "ZG", "ZH", "ZI", "ZJ", "ZQ", "ZT",
+    "V1", "V2", "V3", "V4",
+})
+
+
+def _normalize_country_code(wb_code: str) -> str | None:
+    """Normalize WB country code to ISO alpha-2. Returns None for aggregates."""
+    code = wb_code.strip().upper()
+    if len(code) != 2:
+        return None
+    # Reject codes starting with a digit (e.g. "1W" for World)
+    if code[0].isdigit():
+        return None
+    if code in _WB_AGGREGATE_CODES:
+        return None
+    return code
+
+
+def _fetch_indicator(
+    session: niquests.Session,
+    indicator: str,
+) -> list[dict]:
+    """Fetch all records for one indicator. Returns list of row dicts."""
+    rows: list[dict] = []
+    page = 1
+
+    while page <= MAX_PAGES:
+        url = (
+            f"{WDI_BASE_URL}/{indicator}"
+            f"?format=json&date={DATE_RANGE}&per_page={MAX_PER_PAGE}&page={page}"
+        )
+        logger.info("GET %s page %d", indicator, page)
+        resp = session.get(url, timeout=HTTP_TIMEOUT_SECONDS * 2)
+        resp.raise_for_status()
+
+        data = resp.json()
+        assert isinstance(data, list) and len(data) == 2, (
+            f"unexpected WB response shape for {indicator}: {type(data)}, len={len(data)}"
+        )
+        meta, records = data
+        total_pages = meta.get("pages", 1)
+
+        if records is None:
+            logger.warning("WB returned null data for %s page %d", indicator, page)
+            break
+
+        for record in records:
+            value = record.get("value")
+            if value is None:
+                continue
+            country_code = _normalize_country_code(record["country"]["id"])
+            if country_code is None:
+                continue
+            rows.append({
+                "country_code": country_code,
+                "indicator": indicator,
+                "ref_year": int(record["date"]),
+                "value": float(value),
+            })
+
+        if page >= total_pages:
+            break
+        page += 1
+
+    return rows
+
+
+def extract(
+    landing_dir: Path,
+    year_month: str,
+    conn: sqlite3.Connection,
+    session: niquests.Session,
+) -> dict:
+    """Fetch WDI indicators. Skips if already run this month."""
+    last_cursor = get_last_cursor(conn, EXTRACTOR_NAME)
+    if last_cursor == year_month:
+        logger.info("already have data for %s — skipping", year_month)
+        return {"files_written": 0, "files_skipped": 1, "bytes_written": 0}
+
+    rows: list[dict] = []
+    for indicator in INDICATORS:
+        indicator_rows = _fetch_indicator(session, indicator)
+        logger.info("%s: %d records", indicator, len(indicator_rows))
+        rows.extend(indicator_rows)
+
+    assert len(rows) >= 200, f"expected ≥200 WB records, got {len(rows)} — API may have changed"
+    logger.info("total: %d WDI records", len(rows))
+
+    year, month = year_month.split("/")
+    dest_dir = landing_path(landing_dir, "worldbank", year, month)
+    dest = dest_dir / "wdi_indicators.json.gz"
+    payload = json.dumps({"rows": rows, "count": len(rows)}).encode()
+    bytes_written = write_gzip_atomic(dest, payload)
+    logger.info("written %s bytes compressed", f"{bytes_written:,}")
+
+    return {
+        "files_written": 1,
+        "files_skipped": 0,
+        "bytes_written": bytes_written,
+        "cursor_value": year_month,
+    }
+
+
+def main() -> None:
+    run_extractor(EXTRACTOR_NAME, extract)
+
+
+if __name__ == "__main__":
+    main()