Publish labor demand index for 2026-06-01

README.md

@@ -21,10 +21,18 @@ configs:
   data_files:
   - split: train
     path: jolts_sector_metrics/date=*/part-*.parquet
+- config_name: jolts_release_vintages
+  data_files:
+  - split: train
+    path: jolts_release_vintages/date=*/part-*.parquet
 - config_name: sampled_posting_aggregates
   data_files:
   - split: train
     path: sampled_posting_aggregates/date=*/part-*.parquet
+- config_name: labor_pressure_index
+  data_files:
+  - split: train
+    path: labor_pressure_index/date=*/part-*.parquet
 - config_name: role_taxonomy
   data_files:
   - split: train
@@ -53,9 +61,11 @@ An agent-friendly labor-demand panel for finding where organizations are still t
 
 This dataset intentionally publishes **aggregates and official public-domain series only**:
 
-- `official_labor_timeseries`: BLS JOLTS monthly openings, hires, quits, layoffs/discharges, and separations by US sector.
+- `official_labor_timeseries`: BLS JOLTS monthly openings, hires, quits, layoffs/discharges, and separations by US sector (latest revision).
 - `jolts_sector_metrics`: derived vacancy/hire, quit/hire, separation/hire, and YoY openings metrics from JOLTS.
-- `sampled_posting_aggregates`: small, rate-limited JobSpy query aggregates by role family, geography, and snapshot date.
+- `jolts_release_vintages`: append-only snapshots of JOLTS values as observed on each collection date, partitioned by `vintage_date`, so revision history is preserved (first-release vs. latest).
+- `sampled_posting_aggregates`: small, rate-limited JobSpy query aggregates by role family, geography, and snapshot date. Includes posting-age signals (`new_postings_count`, `median_posting_age_days`, `stale_share_30d/60d/90d`) derived from a private first-seen ledger, and salary/workflow/compliance texture.
+- `labor_pressure_index`: **the combined index** — one auditable score per role family x geo x day that fuses automation fit, workflow digitization, posting persistence (can't-fill signal), and confidence-weighted JOLTS sector tightness. This is the join of the demand and tightness halves of the panel, materialized.
 - `role_taxonomy`: the tracked automatable role families, query terms, skill terms, and automation-fit labels.
 - `query_taxonomy`: one row per tracked search query, so query panels can evolve without changing downstream schemas.
 - `occupation_bridge`: candidate SOC, NAICS, and JOLTS-sector bridges for each tracked workflow.
@@ -76,22 +86,25 @@ print(df.head())
 
 ## Use Cases
 
-- Track sampled demand for automatable back-office roles.
-- Join live posting aggregates to official JOLTS sector tightness.
+- Track sampled demand for automatable back-office roles, including how long requisitions stay open (`stale_share_30d/60d/90d`) — the clearest "can't hire a human" signal.
+- Join live posting aggregates to official JOLTS sector tightness via the `jolts_sector_candidates_json` bridge.
+- Nowcast monthly JOLTS from daily postings, and check whether postings led the *revised* number using `jolts_release_vintages`.
 - Analyze posting-level workflow texture through aggregate software, workflow, and compliance term counts.
-- Find role families where hiring demand, turnover, and automation fit are all high.
+- Find role families where hiring demand, turnover, posting age, and automation fit are all high.
 - Build daily research agents without storing raw scraped job-board content.
 
 ## Limitations
 
-`sampled_posting_aggregates` is a directional sample, not a job-posting census. Job boards rate-limit and change markup. Source coverage can shift by day, site, geography, and query wording. Treat counts as comparable only within the same query/source configuration and use BLS rows as the official macro anchor.
+`sampled_posting_aggregates` is a directional sample, not a job-posting census. Job boards rate-limit and change markup. Source coverage can shift by day, site, geography, and query wording. Treat counts as comparable only within the same query/source configuration and use BLS rows as the official macro anchor. A pinned core panel is sampled every day for stable per-cell time series; the long tail rotates.
 
 ## Layout
 
 ```
 official_labor_timeseries/date=YYYY-MM-DD/part-0000.parquet
 jolts_sector_metrics/date=YYYY-MM-DD/part-0000.parquet
+jolts_release_vintages/date=YYYY-MM-DD/part-0000.parquet
 sampled_posting_aggregates/date=YYYY-MM-DD/part-0000.parquet
+labor_pressure_index/date=YYYY-MM-DD/part-0000.parquet
 role_taxonomy/date=YYYY-MM-DD/part-0000.parquet
 query_taxonomy/date=YYYY-MM-DD/part-0000.parquet
 occupation_bridge/date=YYYY-MM-DD/part-0000.parquet

_manifest.json

@@ -5,6 +5,7 @@
     "geo_taxonomy/date=2026-05-30/part-0000.parquet",
     "geo_taxonomy/date=2026-05-31/part-0000.parquet",
     "geo_taxonomy/date=2026-06-01/part-0000.parquet",
+    "jolts_release_vintages/date=2026-06-01/part-0000.parquet",
     "jolts_sector_metrics/date=2000-12-01/part-0000.parquet",
     "jolts_sector_metrics/date=2001-01-01/part-0000.parquet",
     "jolts_sector_metrics/date=2001-02-01/part-0000.parquet",
@@ -314,6 +315,7 @@
     "jolts_sector_taxonomy/date=2026-05-30/part-0000.parquet",
     "jolts_sector_taxonomy/date=2026-05-31/part-0000.parquet",
     "jolts_sector_taxonomy/date=2026-06-01/part-0000.parquet",
+    "labor_pressure_index/date=2026-06-01/part-0000.parquet",
     "occupation_bridge/date=2026-05-28/part-0000.parquet",
     "occupation_bridge/date=2026-05-29/part-0000.parquet",
     "occupation_bridge/date=2026-05-30/part-0000.parquet",
@@ -639,7 +641,7 @@
     "sampled_posting_aggregates/date=2026-05-31/part-0000.parquet",
     "sampled_posting_aggregates/date=2026-06-01/part-0000.parquet"
   ],
-  "generated_at": "2026-06-01T04:35:29.472561+00:00",
+  "generated_at": "2026-06-01T09:58:36.716359+00:00",
   "public_source_kinds": [
     "derived",
     "public_domain",
@@ -653,6 +655,7 @@
     "geo_taxonomy/2026-05-30": 10,
     "geo_taxonomy/2026-05-31": 10,
     "geo_taxonomy/2026-06-01": 10,
+    "jolts_release_vintages/2026-06-01": 400,
     "jolts_sector_metrics/2000-12-01": 20,
     "jolts_sector_metrics/2001-01-01": 20,
     "jolts_sector_metrics/2001-02-01": 20,
@@ -962,6 +965,7 @@
     "jolts_sector_taxonomy/2026-05-30": 23,
     "jolts_sector_taxonomy/2026-05-31": 23,
     "jolts_sector_taxonomy/2026-06-01": 23,
+    "labor_pressure_index/2026-06-01": 16,
     "occupation_bridge/2026-05-28": 10,
     "occupation_bridge/2026-05-29": 10,
     "occupation_bridge/2026-05-30": 10,
@@ -1285,7 +1289,7 @@
     "sampled_posting_aggregates/2026-05-29": 10,
     "sampled_posting_aggregates/2026-05-30": 10,
     "sampled_posting_aggregates/2026-05-31": 10,
-    "sampled_posting_aggregates/2026-06-01": 10
+    "sampled_posting_aggregates/2026-06-01": 16
   },
   "time_ranges": {
     "geo_taxonomy/2026-05-28": {
@@ -1313,6 +1317,11 @@
       "start": "2026-06-01",
       "timezone": "UTC"
     },
+    "jolts_release_vintages/2026-06-01": {
+      "end": "2026-06-01",
+      "start": "2026-06-01",
+      "timezone": "UTC"
+    },
     "jolts_sector_metrics/2000-12-01": {
       "end": "2000-12-01",
       "start": "2000-12-01",
@@ -2858,6 +2867,11 @@
       "start": "2026-06-01",
       "timezone": "UTC"
     },
+    "labor_pressure_index/2026-06-01": {
+      "end": "2026-06-01",
+      "start": "2026-06-01",
+      "timezone": "UTC"
+    },
     "occupation_bridge/2026-05-28": {
       "end": "2026-05-28",
       "start": "2026-05-28",

_schema.json

@@ -11,6 +11,13 @@
       "geo",
       "geo_type"
     ],
+    "jolts_release_vintages": [
+      "vintage_date",
+      "period",
+      "sector",
+      "measure",
+      "value"
+    ],
     "jolts_sector_metrics": [
       "period",
       "sector",
@@ -24,6 +31,17 @@
       "sector",
       "series_id_example"
     ],
+    "labor_pressure_index": [
+      "snapshot_date",
+      "role_family",
+      "geo",
+      "labor_pressure_score_0_1",
+      "automation_component_0_1",
+      "digital_workflow_component_0_1",
+      "posting_persistence_component_0_1",
+      "sector_tightness_component_0_1",
+      "blended_vacancy_hire_ratio"
+    ],
     "occupation_bridge": [
       "snapshot_date",
       "role_family",
@@ -60,6 +78,9 @@
       "query_id",
       "query",
       "deduped_postings_count",
+      "new_postings_count",
+      "median_posting_age_days",
+      "stale_share_30d",
       "remote_share",
       "salary_mid_mean"
     ]

examples/quickstart.py

@@ -2,6 +2,10 @@ import pandas as pd
 
 repo = "Chainticks/labor-demand-index"
 latest = pd.read_csv(f"https://huggingface.co/datasets/{repo}/resolve/main/LATEST_DATE.txt", header=None).iloc[0, 0]
-url = f"https://huggingface.co/datasets/{repo}/resolve/main/sampled_posting_aggregates/date={latest}/part-0000.parquet"
+
+# The combined index: where automatable work is in demand AND hard to fill.
+url = f"https://huggingface.co/datasets/{repo}/resolve/main/labor_pressure_index/date={latest}/part-0000.parquet"
 df = pd.read_parquet(url)
-print(df.sort_values("deduped_postings_count", ascending=False).head(10))
+print(df.sort_values("labor_pressure_score_0_1", ascending=False)[
+    ["role_family", "geo", "labor_pressure_score_0_1", "blended_vacancy_hire_ratio", "stale_share_30d", "salary_mid_median"]
+].head(10))

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