Error code:   ConfigNamesError
Exception:    ValueError
Message:      Feature type 'Int' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf', 'Nifti', 'Json']
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 612, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2138, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1983, in from_dict
                  obj = generate_from_dict(dic)
                        ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1564, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1570, in generate_from_dict
                  raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
              ValueError: Feature type 'Int' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf', 'Nifti', 'Json']

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Measuring the Intensive Margin of Work: Task-Level Labor Input Data

This dataset provides task-level measures of labor input within occupations, constructed from O*NET task frequency data.

For each occupation–task pair, we estimate:

Task flow (μ): expected number of times a task is performed annually per worker within an occupation
Task share (π): proportion of total labor input allocated to the task

Under a homogeneous task-duration assumption, task shares can be interpreted as time allocations across tasks within occupations.

Unlike standard O*NET-derived measures, this dataset provides statistically specified estimators of task-level labor input, including uncertainty (variance) for incumbent-based estimates.

These measures enable:

AI exposure measurement
workforce decomposition
automation targeting
task-level economic analysis

Measurement Units

Task flow (μ) Expected annual number of task occurrences per worker within an occupation
Task share (π) Fraction of total labor input allocated to a task within an occupation

Interpretable as a time share under the assumption of homogeneous task duration.

Data Files

Data is organized by:

O*NET version
estimate type (mean vs full)
measure (flow vs share)

Mean estimates (point estimates only)

Task flow (μ): task_labor_input_mean_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_flow_mean_estimates.csv
Task labor input share (π): task_labor_input_mean_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_labor_input_mean_estimates.csv

Full estimates (mean + variance)

Task flow (μ): task_labor_input_full_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_flow_full_estimates.csv
Task labor input share (π): task_labor_input_full_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_labor_input_full_estimates.csv

Mean vs Full Estimates

Mean estimates Combine incumbent and analyst task ratings and report point estimates only.
Full estimates Rely exclusively on incumbent survey data, which provides frequency distributions with sampling uncertainty. This enables construction of fully specified estimators, including:
- mean (μ or π)
- variance (Var)

Full estimates therefore support statistical inference and uncertainty quantification, while mean estimates provide broader coverage.

Dataset Structure

Each dataset is defined at the occupation–task level, with one row per (onetsoc_code, task_id) pair.

Columns

Mean estimates:

onetsoc_code — O*NET occupation code
task_id — O*NET task identifier
mean — Estimated value (μ or π)

Full estimates:

onetsoc_code — O*NET occupation code
task_id — O*NET task identifier
mean — Estimated value (μ or π)
variance — Estimated variance of the estimator

Data Source: O*NET

O*NET task frequency data is based on:

Incumbents (survey respondents) Provide frequency distributions with associated standard errors
Analysts (occupation experts) Provide point estimates without measures of dispersion

This dataset distinguishes between:

combined estimates (mean only)
incumbent-based estimates (mean + variance)

Coverage

The dataset includes multiple O*NET releases (from version 20.1 onward).

Each version may differ due to:

updates to task definitions
changes in occupation coverage
new survey responses

⚠️ Version comparability note O*NET releases are not strictly comparable over time. Differences across versions may reflect survey and taxonomy updates rather than true economic changes.

Quick Start

import pandas as pd


job_task_input_mean_estimates = pd.read_csv(
    "https://huggingface.co/datasets/MIT-WAL/job_task_input_share/resolve/main/task_labor_input_mean_estimates/30_2/ONET_30_2_weight_mode_STANDARD_task_labor_input_mean_estimates.csv"
)

Example Applications

AI exposure measurement at the task level
Workforce decomposition into task bundles
Construction of task-based production functions

Limitations

Task durations are assumed homogeneous when constructing task shares
Task flows rely on discretized frequency bins (midpoint approximation)
Full estimates are limited to incumbent-based tasks
Measurement error arises from survey sampling and bin approximation
Cross-version comparisons should be interpreted with caution

Citation

If you use this dataset, please cite:

Bouquet, Pierre and Sheffi, Yossi (2026). Measuring the Intensive Margin of Work: Task Shares and Concentration. MIT Center for Transportation & Logistics Research Paper No. 2026/004.

SSRN: https://ssrn.com/abstract=6174538 DOI: http://dx.doi.org/10.2139/ssrn.6174538

License

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/

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