Datasets:

Cespial
/

adi

---
license: cc-by-4.0
pretty_name: "Agent Delegability Index (ADI) v2026.1"
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - tabular-classification
  - text-classification
tags:
  - ai-agents
  - delegation
  - automation
  - labor-economics
  - occupations
  - isco-08
  - onet
  - ilostat
  - economics
  - llm-as-a-judge
  - delegation-divide
configs:
  - config_name: adi_isco
    default: true
    data_files:
      - split: train
        path: adi_isco.parquet
  - config_name: adi_pais
    data_files:
      - split: train
        path: adi_pais.parquet
  - config_name: adi_tarea
    data_files:
      - split: train
        path: adi_tarea.parquet
---
# Agent Delegability Index (ADI) v2026.1

**35.2 % of task time in world employment is at least partly delegable to AI agents (L2 or L3) in 2026; only 2.5 % can
be handed over in full (L3).** Delegable employment rises with income — 22.5 % in low-income, 28.3 % in lower-middle,
35.0 % in upper-middle and 48.7 % in high-income countries — the *delegation divide* (7.23 percentage points per log point
of GDP per capita, 146 countries).

Dataset of the paper *The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries*
(Espinal Maya & Jiménez Londoño, 2026). Preregistered at OSF: [doi:10.17605/OSF.IO/9ZNTF](https://doi.org/10.17605/OSF.IO/9ZNTF).
Explore every occupation and country, with charts and citations: **<https://balamlabs.org/adi>** (Balam Labs). Paper: [arXiv:2610.30752](https://arxiv.org/abs/2610.30752).
Dataset DOI: [10.5281/zenodo.23552918](https://doi.org/10.5281/zenodo.23552918). Code, rubric, MCP server and full pipeline: <https://github.com/Cespial/delegation-divide>.

## Configs

| config | rows | unit |
|---|---:|---|
| `adi_isco` (default) | 433 | ISCO-08 unit group (4 digits) |
| `adi_pais` | 146 | country (ISO 3166-1 alpha-3) |
| `adi_tarea` | 18,796 | O\*NET 30.2 task statement, with each model's level |
```python
from datasets import load_dataset
occ = load_dataset("cespial/adi", "adi_isco", split="train")
countries = load_dataset("cespial/adi", "adi_pais", split="train")
tasks = load_dataset("cespial/adi", "adi_tarea", split="train")
```
The repository also carries the same tables as CSV, `claims.jsonl` (every number in the paper), the raw model outputs
(`ratings_sonnet.jsonl.gz`, `ratings_haiku.jsonl.gz`), a static JSON API (`api/isco/<code>.json`,
`api/country/<ISO3>.json`, `api/index.json`), `DATA_DICTIONARY.md` and `SHA256SUMS`.
## Rating scale (verbatim from the frozen rubric)
- **L0 — No meaningful role.** The agent cannot perform or materially speed up any substantial part of the task.
- **L1 — Augmentation.** A human performs the task; the AI assists step by step (drafts, searches, suggestions). The human
  stays in the loop at each step and produces the output.
- **L2 — Partial delegation.** An agent can execute substantial sub-steps of the task end to end from a goal
  specification. A human still has to perform at least one essential step or review and correct the work before use.
- **L3 — Full delegation.** An agent can complete the whole task end to end, to the quality a competent worker would
  accept, given only the goal and access. The human's role is limited to assigning it and accepting the result.
Reference technology: publicly available AI agents as of October 2026 (tool use, code execution, browsing, computer use,
file handling; no physical body). Ratings measure technical feasibility, not adoption or legality. Each task also carries
the share of working time an agent could take over, the minutes a human needs, and four barrier flags (physical,
accountability, trust, access).
## Main columns
- `adi_isco`: `isco08`, `titulo`, `adi_l2` (share 0–1 of task weight at L2+), `adi_l3` (share at L3), `adi_share` (% of
  working time delegable), `n_tareas`, `barrera_{fisica,responsabilidad,confianza,acceso}`, `ic_lo`/`ic_hi` (95 % bootstrap
  interval of `adi_share`).
- `adi_pais`: `iso3`, `pais`, `ingreso` (World Bank FY2026), `anio`, `resolucion` (ISCO level of the employment data),
  `adi_l2`, `adi_l3`, `adi_share` (employment-weighted), `empleo_total`.
- `adi_tarea`: `task_id` (O\*NET), `titulo`, `texto`, consensus `nivel`, `share`, `minutos`, barrier fractions,
  `nivel_sonnet`, `nivel_haiku`.

Types, units and definitions for every column: `DATA_DICTIONARY.md`.

## How it was built

Raters: Claude Sonnet 5.5 (primary) and Claude Haiku 5.5 (secondary), one task per call, neutral configuration, rubric
hash checked before every run. Consensus: lower level when the models disagree (quadratic-weighted κ = 0.77 between them).
Tasks are weighted by O\*NET importance × frequency and carried to ISCO-08 through O\*NET-SOC 2019 → SOC 2010 → ISCO-08
(433 of 436 unit groups). Country scores weight unit groups by ILOSTAT employment (latest year ≥ 2019, 2-digit or 1-digit
ISCO) with within-group four-digit weights from BLS OES (high income) or Colombia's GEIH (other groups).

## Limitations

- Ratings come from two models of one developer; a blind human rating of 300 tasks agrees with the model consensus at
  kappa = 0.85, and an agentic panel on 1,500 tasks at kappa = 0.87 (see the paper, section 7).
- O\*NET describes work in the United States; the same task mix is carried to every country, so cross-country
  differences are compositional.
- Technical delegability is an upper bound on delegation, not a forecast of employment effects.
## Licence and attribution
CC BY 4.0. Includes information from the O\*NET 30.2 Database by the U.S. Department of Labor, Employment and Training
Administration (USDOL/ETA), used under the CC BY 4.0 license (task statements are reproduced verbatim in `adi_tarea`).
ESCO, ILOSTAT, World Bank, BLS and DANE inputs are used only as derived aggregates.

## Citation

```bibtex
@misc{espinal2026delegation,
  title         = {The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries},
  author        = {Espinal Maya, Cristian and Jim{\'e}nez Londo{\~n}o, Santiago},
  year          = {2026},
  eprint        = {2610.30752},
  archivePrefix = {arXiv},
  primaryClass  = {econ.GN},
  doi           = {10.48550/arXiv.2610.30752},
  url           = {https://arxiv.org/abs/2610.30752}
}
```

	---
	license: cc-by-4.0
	pretty_name: "Agent Delegability Index (ADI) v2026.1"
	language:
	- en
	size_categories:
	- 10K<n<100K
	task_categories:
	- tabular-classification
	- text-classification
	tags:
	- ai-agents
	- delegation
	- automation
	- labor-economics
	- occupations
	- isco-08
	- onet
	- ilostat
	- economics
	- llm-as-a-judge
	- delegation-divide
	configs:
	- config_name: adi_isco
	default: true
	data_files:
	- split: train
	path: adi_isco.parquet
	- config_name: adi_pais
	data_files:
	- split: train
	path: adi_pais.parquet
	- config_name: adi_tarea
	data_files:
	- split: train
	path: adi_tarea.parquet
	---

	# Agent Delegability Index (ADI) v2026.1

	**35.2 % of task time in world employment is at least partly delegable to AI agents (L2 or L3) in 2026; only 2.5 % can
	be handed over in full (L3).** Delegable employment rises with income — 22.5 % in low-income, 28.3 % in lower-middle,
	35.0 % in upper-middle and 48.7 % in high-income countries — the delegation divide (7.23 percentage points per log point
	of GDP per capita, 146 countries).

	Dataset of the paper The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries
	(Espinal Maya & Jiménez Londoño, 2026). Preregistered at OSF: [doi:10.17605/OSF.IO/9ZNTF](https://doi.org/10.17605/OSF.IO/9ZNTF).
	Explore every occupation and country, with charts and citations: <https://balamlabs.org/adi> (Balam Labs). Paper: [arXiv:2610.30752](https://arxiv.org/abs/2610.30752).
	Dataset DOI: [10.5281/zenodo.23552918](https://doi.org/10.5281/zenodo.23552918). Code, rubric, MCP server and full pipeline: <https://github.com/Cespial/delegation-divide>.

	## Configs

	\| config \| rows \| unit \|
	\|---\|---:\|---\|
	\| `adi_isco` (default) \| 433 \| ISCO-08 unit group (4 digits) \|
	\| `adi_pais` \| 146 \| country (ISO 3166-1 alpha-3) \|
	\| `adi_tarea` \| 18,796 \| O\*NET 30.2 task statement, with each model's level \|

	```python
	from datasets import load_dataset
	occ = load_dataset("cespial/adi", "adi_isco", split="train")
	countries = load_dataset("cespial/adi", "adi_pais", split="train")
	tasks = load_dataset("cespial/adi", "adi_tarea", split="train")
	```

	The repository also carries the same tables as CSV, `claims.jsonl` (every number in the paper), the raw model outputs
	(`ratings_sonnet.jsonl.gz`, `ratings_haiku.jsonl.gz`), a static JSON API (`api/isco/<code>.json`,
	`api/country/<ISO3>.json`, `api/index.json`), `DATA_DICTIONARY.md` and `SHA256SUMS`.

	## Rating scale (verbatim from the frozen rubric)

	- L0 — No meaningful role. The agent cannot perform or materially speed up any substantial part of the task.
	- L1 — Augmentation. A human performs the task; the AI assists step by step (drafts, searches, suggestions). The human
	stays in the loop at each step and produces the output.
	- L2 — Partial delegation. An agent can execute substantial sub-steps of the task end to end from a goal
	specification. A human still has to perform at least one essential step or review and correct the work before use.
	- L3 — Full delegation. An agent can complete the whole task end to end, to the quality a competent worker would
	accept, given only the goal and access. The human's role is limited to assigning it and accepting the result.

	Reference technology: publicly available AI agents as of October 2026 (tool use, code execution, browsing, computer use,
	file handling; no physical body). Ratings measure technical feasibility, not adoption or legality. Each task also carries
	the share of working time an agent could take over, the minutes a human needs, and four barrier flags (physical,
	accountability, trust, access).

	## Main columns

	- `adi_isco`: `isco08`, `titulo`, `adi_l2` (share 0–1 of task weight at L2+), `adi_l3` (share at L3), `adi_share` (% of
	working time delegable), `n_tareas`, `barrera_{fisica,responsabilidad,confianza,acceso}`, `ic_lo`/`ic_hi` (95 % bootstrap
	interval of `adi_share`).
	- `adi_pais`: `iso3`, `pais`, `ingreso` (World Bank FY2026), `anio`, `resolucion` (ISCO level of the employment data),
	`adi_l2`, `adi_l3`, `adi_share` (employment-weighted), `empleo_total`.
	- `adi_tarea`: `task_id` (O\*NET), `titulo`, `texto`, consensus `nivel`, `share`, `minutos`, barrier fractions,
	`nivel_sonnet`, `nivel_haiku`.

	Types, units and definitions for every column: `DATA_DICTIONARY.md`.

	## How it was built

	Raters: Claude Sonnet 5.5 (primary) and Claude Haiku 5.5 (secondary), one task per call, neutral configuration, rubric
	hash checked before every run. Consensus: lower level when the models disagree (quadratic-weighted κ = 0.77 between them).
	Tasks are weighted by O\NET importance × frequency and carried to ISCO-08 through O\NET-SOC 2019 → SOC 2010 → ISCO-08
	(433 of 436 unit groups). Country scores weight unit groups by ILOSTAT employment (latest year ≥ 2019, 2-digit or 1-digit
	ISCO) with within-group four-digit weights from BLS OES (high income) or Colombia's GEIH (other groups).

	## Limitations

	- Ratings come from two models of one developer; a blind human rating of 300 tasks agrees with the model consensus at
	kappa = 0.85, and an agentic panel on 1,500 tasks at kappa = 0.87 (see the paper, section 7).
	- O\*NET describes work in the United States; the same task mix is carried to every country, so cross-country
	differences are compositional.
	- Technical delegability is an upper bound on delegation, not a forecast of employment effects.

	## Licence and attribution

	CC BY 4.0. Includes information from the O\*NET 30.2 Database by the U.S. Department of Labor, Employment and Training
	Administration (USDOL/ETA), used under the CC BY 4.0 license (task statements are reproduced verbatim in `adi_tarea`).
	ESCO, ILOSTAT, World Bank, BLS and DANE inputs are used only as derived aggregates.

	## Citation

	```bibtex
	@misc{espinal2026delegation,
	title = {The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries},
	author = {Espinal Maya, Cristian and Jim{\'e}nez Londo{\~n}o, Santiago},
	year = {2026},
	eprint = {2610.30752},
	archivePrefix = {arXiv},
	primaryClass = {econ.GN},
	doi = {10.48550/arXiv.2610.30752},
	url = {https://arxiv.org/abs/2610.30752}
	}
	```