MIT-WAL/job_task_input_share

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

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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.

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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

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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.

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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

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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)

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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.

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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"
)

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Example Applications

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

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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

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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

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License

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