officeblues/misery-data

Misery Data — the cost of work, in numbers

Two small, clean, public datasets on what work actually costs you — maintained by Office Blues, a tools-and-data project for the unhappily employed. Built from federal sources, free to reuse, easy to cite.

CC BY 4.0 — quote a figure, build on it, cite us. No scraping mystery; every number traces to a public source.

---

📦 What's inside

Config	Rows	What it is
occupation_wages	823	Annual wage percentiles for U.S. detailed occupations (BLS OEWS, May 2025)
city_burnout_index	50	A composite burnout score (0–100) for the 50 largest U.S. metros

Both ship as CSV and JSON, plus a Frictionless Data datapackage.json (typed schemas + licensing).

🚀 Quickstart

from datasets import load_dataset

wages = load_dataset("officeblues/misery-data", "occupation_wages", split="train")
burnout = load_dataset("officeblues/misery-data", "city_burnout_index", split="train")

print(wages[0])
# {'soc': '15-1252', 'title': 'Software Developers', 'median_usd': 132270, ...}

Prefer pandas?

import pandas as pd
url = "https://huggingface.co/datasets/officeblues/misery-data/resolve/main/data/occupation_wages.csv"
df = pd.read_csv(url)
df.nlargest(10, "median_usd")[["title", "median_usd"]]

---

🗂️ Schema

occupation_wages — U.S. occupation annual wages (n = 823)

field	type	meaning
soc	string	Standard Occupational Classification (SOC 2018) code
title	string	Occupation title
p25_usd / median_usd / p75_usd / p90_usd	int	Annual wage percentiles, USD
source_url	string	The Office Blues page for this occupation

Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2025 release. National figures.

city_burnout_index — City burnout index (n = 50)

field	type	meaning
cbsa_code	string	Census CBSA code
city / state	string	Metro area / state
population	int	Metro population
burnout_score	int	Composite score, 0–100 (higher = worse)
rank / of_cities	int	Rank (1 = worst) of 50
source_url	string	The Office Blues page for this metro

Methodology: the burnout score composites public metro-level indicators (pay-to-cost-of-living gap, commute time, unemployment). Full method: officeblues.net/methodology. Treat it as an editorial index (v1), not an official statistic.

---

📑 Citation

DOI: 10.5281/zenodo.20534485 — the concept DOI, always resolving to the latest version (archived on Zenodo).

@misc{officeblues_misery_data,
  author       = {Office Blues},
  title        = {Misery Data: U.S. occupation wages and a city burnout index},
  year         = {2026},
  publisher    = {Office Blues},
  doi          = {10.5281/zenodo.20534485},
  url          = {https://officeblues.net},
  note         = {CC BY 4.0}
}

ℹ️ About

Maintained by Office Blues — https://officeblues.net. The methodology and the tools behind these numbers (the Meeting Tax Calculator, the Salary Negotiation Script, the daily pulse) live there. Aggregates only — no personal or per-visitor data. This is the canonical mirror of github.com/officeblues/misery-data.

Tools, data & receipts for the unhappy employed.