Global Automation Atlas

Public replication package for Global Automation Atlas by Prashant Garg, Tommaso Crosta, and Jasmin Baier.

Atlas website | Paper | Data downloads

This repository contains the retained labels, analysis panels, prompt protocols, source-data files, and R scripts used to check the paper results. The measurement object is a task-country dataset: 18,797 O*NET tasks evaluated across 124 country contexts, producing 2,330,776 retained task-country observations.

The package does not include exploratory drafts, old figure variants, raw API logs, or the full language-model rationale corpus. For the rationale-concept analysis reported in the paper, it includes compact source-data tables for the fixed concepts, fidelity audit, evaluation-sample paired tests, and manuscript figure/table source data.

Quick start

From the package root, first check that the expected files are present:

Rscript code/check_replication_package.R

Then run the reproduction workflow:

Rscript code/make_all.R

The workflow writes rebuilt files to reproduced/. It also runs a numeric audit of paper-presented values. If any audited number fails to match after rounding, the script stops and points to reproduced/checks/numeric_claim_audit.csv.

What the scripts check

• code/check_replication_package.R checks the core inventory and prints row counts for the task-country label file.
• code/make_all.R rebuilds summary tables and selected figures from the included data.
• code/04_numeric_audit.R checks headline values reported in the paper, including country exposure ranges, validation correlations, gender-gap summaries, and fixed-effect coefficients.
• code/05_rationale_concept_audit.R checks the rationale-concept source bundle used for the same-task country-conditioning figure and appendix table.
• reproduced/checks/figure_reproduction_status.csv records which manuscript figures are rebuilt from source data and which are included as final manuscript figure files.

At the time of this package build, the numeric audit checks 53 rounded paper values.

Folder guide

• prompts/ contains the country-conditioned, income-group, and context-free prompt protocols, plus model notes.
• data_intermediate/ contains the retained measurement outputs used downstream. The central file is task_country_labels_analysis.parquet, with 2,330,776 task-country observations across 124 countries and 18,797 tasks.
• data_analysis/ contains smaller panels used directly in the country, channel, predictor, occupation, industry, and gender analyses.
• outputs/source_data/ contains source data for paper and supplementary figures and tables where a compact source file is available.
• outputs/source_data/rationale_concepts/ contains the fixed concept list, fidelity summaries, evaluation-sample paired tests, and source data for the rationale-concept analysis.
• outputs/figures/ and outputs/tables/ contain the final manuscript figure and table files copied from the submitted paper folder.
• reproduced/ is created by code/make_all.R and contains rebuilt checks, tables, and figures.
• docs/data_dictionary/ gives column descriptions and types for the main public data files.
• data_raw_public_metadata/source_inventory.csv lists public source datasets and links.

Language-model labelling step

The package documents the language-model labelling protocol but does not rerun it. The API stage depends on model availability, model versions, and paid external services. For reproducibility, the package supplies the retained labels used in the paper and the prompts needed to inspect the measurement protocol.

The prompt files are:

• prompts/country_conditioned_prompt.md
• prompts/income_group_prompt.md
• prompts/context_free_prompt.md
• prompts/model_config.json

The retained label file excludes the full rationale text. It keeps only the columns needed for the paper analyses: exposure level, labour margin, channel, AI materiality, AI function, country identifiers, and task identifiers. The compact rationale-concept source bundle in outputs/source_data/rationale_concepts/ documents the subset of rationale-derived results reported in the paper without releasing raw API logs or the full rationale corpus.

Data dictionaries

The main dictionary is docs/data_dictionary/schema_with_descriptions.csv. It reports file names, column names, inferred column types, and descriptions for the main files in data_intermediate/ and data_analysis/. A shorter hand-written dictionary is also available at docs/data_dictionary/data_dictionary.csv.

Figure and table reproduction

The package contains two kinds of figure files.

First, outputs/figures/ contains the exact figure files used by the paper. These files are included so that readers can recover the submitted visual evidence exactly.

Second, reproduced/figures/ contains figures rebuilt from the included source data. These rebuilt figures check the underlying numbers but are not meant to be pixel-identical to the designed manuscript figures. The file reproduced/checks/figure_reproduction_status.csv identifies the status of each manuscript figure.

The same logic applies to tables. Final manuscript table files are in outputs/tables/; rebuilt check tables are in reproduced/tables/.

Data sources

The analysis uses public source data from O*NET, the World Bank, Penn World Table, Barro-Lee, Worldwide Governance Indicators, CEPII BACI, ILOSTAT, the IMF AI Preparedness Index, Eurostat, and external exposure measures cited in the paper. The file data_raw_public_metadata/source_inventory.csv lists source links and roles in the analysis.

Requirements

The reproduction scripts are written in R. They use arrow, dplyr, readr, tidyr, ggplot2, and scales. The package was checked with R on macOS. No API keys are needed.

Software checklist details

The package provides source code rather than compiled standalone software. It includes the retained task-country labels, compact analysis panels, and source-data files needed to demo and reproduce the reported checks.

Tested environment:

• macOS 26.1
• R 4.5.3
• arrow 23.0.1.2
• dplyr 1.2.1
• readr 2.2.0
• tidyr 1.3.2
• ggplot2 4.0.2
• scales 1.4.0

No non-standard hardware is required. A normal desktop or laptop is sufficient. No API keys are required for the public reproduction workflow.

To install the required R packages, run:

install.packages(c("arrow", "dplyr", "readr", "tidyr", "ggplot2", "scales"))

Typical install time on a normal desktop computer is under 10 minutes when binary packages are available, but can be longer if arrow must be compiled from source.

To run the demo inventory check:

Rscript code/check_replication_package.R

Expected output reports the number of task-country labels, figures, tables, and retained rationale concepts. This check normally runs in under one minute.

To reproduce the included tables, selected figures, and numerical audits:

Rscript code/make_all.R

Expected output is written to reproduced/, including rebuilt tables, selected rebuilt figures, reproduced/checks/numeric_claim_audit.csv, reproduced/checks/figure_reproduction_status.csv, and reproduced/checks/rationale_concept_audit.csv. On a recent desktop, the workflow usually runs in a few minutes.

Known limits

The package is designed to reproduce the paper results from retained labels, not to repeat the original API labelling run. Some final manuscript figures are included as exact submitted files while their full plotting scripts remain outside this public package. The status file in reproduced/checks/ makes this distinction explicit.

Citation

If you use this package, please cite the paper. Citation metadata are provided in CITATION.cff. The current paper version is available at automationatlas.org/paper.

Licence and third-party data terms

The code in this package is released under the MIT License; see LICENSE. The data files combine original constructed measures with cleaned extracts or derived variables from public third-party sources. Those source datasets remain governed by their own terms. See docs/DATA_USE_NOTICE.md for source-specific notes and attribution guidance.