README.md

metadata

title: Monthly Employed Persons by Occupation and DAIOEs
emoji: 🌍
colorFrom: yellow
colorTo: indigo
sdk: docker
pinned: false
license: mit

Monthly Employed Persons by Occupation and DAIOEs

Live app on Hugging Face Spaces

Overview

This repository builds and deploys the AI Exposure & Employment Dashboard — an interactive Shiny app for exploring monthly Swedish employment by occupation alongside DAIOE (Dynamic AI Occupational Exposure Index) scores.

DAIOE measures the potential applicability of AI to occupational content over time. It tracks annual progress across nine AI subdomains (games, vision, language, speech) and links capability advances to occupational requirements via O*NET abilities, weighted by ability importance and social-skill intensity. It is a measure of AI exposure, not adoption or automation probability. See the AI-Econ Lab for full methodology.

The dashboard filters observations by year, sex, occupation, DAIOE metric, and employment-change horizon, then shows summary value boxes (average AI exposure, median employment change, observation count), a Plotly scatter plot with an OLS trendline, and a filterable data table.

Runtime Files

The deployable app is intentionally small:

• app.py defines the Shiny Express app.
• _brand.yml defines the Shiny theme and points to the lab logo.
• logos/lab.svg is shown in the app sidebar and README.
• data/scb_months_lvl1.parquet is the app dataset.
• Dockerfile, .dockerignore, pyproject.toml, and uv.lock define the containerized runtime.

Dependencies

Key Python packages (see pyproject.toml for pinned versions):

Package	Purpose
shiny[theme]	Shiny for Python web framework
shinywidgets	Plotly widget integration
plotly	Interactive scatter plots
polars	Fast dataframe operations
pandas	DataFrame conversion for Plotly
pyarrow	Parquet file I/O
statsmodels	OLS trendline in scatter plot

Requires Python ≥ 3.14.

Local Development

Install dependencies with uv:

uv sync

Run the app locally:

uv run shiny run app.py --reload

Or run with the project virtual environment directly:

.venv/bin/python -m shiny run app.py --reload

Docker

Build the image:

docker build -t ai-econ-daioe-months .

Run it locally:

docker run --rm -p 7860:7860 ai-econ-daioe-months

The container serves the app on http://127.0.0.1:7860.

Branch Workflow

This repository uses separate branches for each stage of the data and deployment pipeline.

Branch	Purpose	Main output
scb_pull	Pulls or prepares the monthly SCB employment data. The SCB Pull -> DAIOE Pull workflow runs main.py on this branch.	data/scb_months.parquet
daioe_pull	Receives the SCB output and enriches or merges it with DAIOE exposure data. The DAIOE Pull -> Development workflow runs main.py here.	data/scb_months_lvl1.parquet
development	Integration branch for the merged dataset and deployable app files before promotion. The Development -> Main workflow promotes the deploy bundle from here.	deploy-ready app files
main	Production/deployment branch. This branch contains the Dockerized Shiny app and syncs to Hugging Face Spaces.	running dashboard

The pipeline is therefore:

scb_pull -> daioe_pull -> development -> main -> Hugging Face Spaces

GitHub Actions

The repository contains four workflows:

• .github/workflows/01_scb_pull_to_daioe_pull.yml builds the base SCB parquet and pushes it to daioe_pull.
• .github/workflows/02_daioe_pull_to_development.yml builds the DAIOE-enriched parquet and pushes it to development.
• .github/workflows/03_development_to_main.yml promotes deployable app files to main.
• .github/workflows/sync_to_hub.yml syncs main to the Hugging Face Space joseph-data/app_months.

The scheduled workflows run daily at 00:00 UTC, and each can also be run manually with workflow_dispatch.

Data Shape

The app dataset (data/scb_months_lvl1.parquet) has monthly occupation-level rows with:

• year, month, sex, occupation, emp_count — dimensions and employment count
• pct_chg_1m, pct_chg_3m, pct_chg_6m — percentage employment change over 1, 3, and 6 months

DAIOE weighted-average exposure columns (daioe_*_wavg) used as x-axis metrics:

Column	Label
daioe_allapps_wavg	All AI Applications
daioe_stratgames_wavg	Strategic Games
daioe_videogames_wavg	Video Games
daioe_imgrec_wavg	Image Recognition
daioe_imgcompr_wavg	Image Compression
daioe_imggen_wavg	Image Generation
daioe_readcompr_wavg	Reading Comprehension
daioe_lngmod_wavg	Language Models
daioe_translat_wavg	Translation
daioe_speechrec_wavg	Speech Recognition
daioe_genai_wavg	Generative AI