Add QCEW CSV module, ECEC breakdown, response caching + typed errors

Item 1 — QCEW county/industry detail:
- new bls_client/qcew.py: thin client over the QCEW Open Data CSV service
  (area/industry/size slices; no API key, no quota), with filter_rows helper
- covers the county/industry detail the timeseries API can't reach

Item 2 — ECEC benefit breakdown:
- new series.ecec() builder from the authoritative cm.estimate decode table
- real helpers: wages, total benefits, paid leave, supplemental pay,
  health insurance, retirement & savings, legally required + ecec_dashboard()
- FIX: ecec_total_benefits was CMU1036... (education/health industries only);
  correct all-civilian total benefits is CMU1030000000000D

Item 3 — caching + typed errors:
- bls_client/cache.py FileCache (on-disk, TTL); BLSClient(cache=True),
  queries_used counter (cache hits don't spend quota)
- bls_client/errors.py: BLSError / BLSQuotaError / BLSRequestError; quota
  exhaustion now raises a clear BLSQuotaError instead of a generic RuntimeError

Tests: 119 offline (+24) incl. ECEC goldens, QCEW fixture parse, cache/quota
behavior; live smoke extended to ECEC + QCEW. Helper sweep: 96/96 live.
README: caching/QCEW usage, updated coverage + limitations.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

bls_client/qcew.py

@@ -0,0 +1,74 @@
+"""QCEW Open Data Access — county/industry employment & wages.
+
+The BLS *timeseries* API only carries QCEW national/state totals. The full
+QCEW detail (every county, every NAICS industry, establishment counts and
+wages) lives behind a separate CSV service that needs no API key and counts
+against no quota:
+
+    https://data.bls.gov/cew/data/api/{year}/{qtr}/{slice}/{code}.csv
+
+This module is a thin wrapper over that service. It is deliberately separate
+from BLSClient (different API, CSV not JSON, no key). Each call returns a list
+of plain dict rows (the CSV columns), so it composes with csv/pandas directly.
+
+Quarter argument: 1-4 for a specific quarter, or "a" for annual averages.
+Area FIPS examples: "US000" national, "11000" DC (state), "11001" a county,
+metro codes start with "C". Industry codes are NAICS (e.g. "10" = all
+industries, "722" = food services, "622" = hospitals).
+"""
+import csv
+import io
+
+import requests
+
+QCEW_BASE = "https://data.bls.gov/cew/data/api"
+_HEADERS = {"User-Agent": "bls-data-library (https://gitea.doesworks.net/giteaadmin/bls-data)"}
+
+# A few common columns, for reference / convenience:
+#   area_fips, own_code, industry_code, agglvl_code, size_code, year, qtr,
+#   annual_avg_estabs, annual_avg_emplvl, total_annual_wages, annual_avg_wkly_wage,
+#   avg_annual_pay  (quarterly slices use month1_emplvl/qtrly_estabs/total_qtrly_wages, etc.)
+
+OWNERSHIP = {
+    "total": "0", "federal": "1", "state": "2", "local": "3",
+    "private": "5", "government": "8",
+}
+
+
+def _fetch_csv(url: str) -> list[dict]:
+    r = requests.get(url, timeout=60, headers=_HEADERS)
+    r.raise_for_status()
+    return parse_csv(r.text)
+
+
+def parse_csv(text: str) -> list[dict]:
+    """Parse QCEW CSV text into a list of row dicts (BLS quotes every field)."""
+    return list(csv.DictReader(io.StringIO(text)))
+
+
+def area(area_fips: str, year: int, qtr="a") -> list[dict]:
+    """Every industry × ownership for one area (county/state/metro/national)."""
+    return _fetch_csv(f"{QCEW_BASE}/{year}/{qtr}/area/{area_fips}.csv")
+
+
+def industry(industry_code: str, year: int, qtr="a") -> list[dict]:
+    """One NAICS industry across all areas."""
+    return _fetch_csv(f"{QCEW_BASE}/{year}/{qtr}/industry/{industry_code}.csv")
+
+
+def size(size_code: str, year: int) -> list[dict]:
+    """Establishment-size breakdown (size data is published for Q1 only)."""
+    return _fetch_csv(f"{QCEW_BASE}/{year}/1/size/{size_code}.csv")
+
+
+def filter_rows(rows, own_code=None, industry_code=None, agglvl_code=None) -> list[dict]:
+    """Filter QCEW rows by ownership / industry / aggregation-level code."""
+    out = rows
+    if own_code is not None:
+        own = OWNERSHIP.get(own_code, own_code)
+        out = [r for r in out if r.get("own_code") == own]
+    if industry_code is not None:
+        out = [r for r in out if r.get("industry_code") == industry_code]
+    if agglvl_code is not None:
+        out = [r for r in out if r.get("agglvl_code") == agglvl_code]
+    return out