| # Measuring the Intensive Margin of Work: Task-Level Labor Input Data |
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| This dataset provides **task-level measures of labor input within occupations**, constructed from O*NET task frequency data. |
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| For each occupation–task pair, we estimate: |
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| * **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 |
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| Under a homogeneous task-duration assumption, task shares can be interpreted as **time allocations across tasks within occupations**. |
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| Unlike standard O*NET-derived measures, this dataset provides **statistically specified estimators of task-level labor input**, including uncertainty (variance) for incumbent-based estimates. |
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| These measures enable: |
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| * AI exposure measurement |
| * workforce decomposition |
| * automation targeting |
| * task-level economic analysis |
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| ## Measurement Units |
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| * **Task flow (μ)** |
| Expected annual number of task occurrences **per worker within an occupation** |
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| * **Task share (π)** |
| Fraction of total labor input allocated to a task within an occupation |
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| Interpretable as a time share under the assumption of homogeneous task duration. |
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| ## Data Files |
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| Data is organized by: |
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| * O*NET version |
| * estimate type (mean vs full) |
| * measure (flow vs share) |
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| ### Mean estimates (point estimates only) |
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| * Task flow (μ): |
| `task_labor_input_mean_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_flow_mean_estimates.csv` |
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| * Task labor input share (π): |
| `task_labor_input_mean_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_labor_input_mean_estimates.csv` |
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| ### Full estimates (mean + variance) |
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| * Task flow (μ): |
| `task_labor_input_full_estimates/{ONET_VERSION}/ONET_{ONET_VERSION}_weight_mode_STANDARD_task_flow_full_estimates.csv` |
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| * 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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| --- |
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| ## Mean vs Full Estimates |
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| * **Mean estimates** |
| Combine incumbent and analyst task ratings and report **point estimates only**. |
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| * **Full estimates** |
| Rely exclusively on **incumbent survey data**, which provides frequency distributions with sampling uncertainty. |
| This enables construction of **fully specified estimators**, including: |
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| * mean (μ or π) |
| * variance (Var) |
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| Full estimates therefore support **statistical inference and uncertainty quantification**, while mean estimates provide broader coverage. |
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| ## Dataset Structure |
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| Each dataset is defined at the **occupation–task level**, with one row per `(onetsoc_code, task_id)` pair. |
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| ### Columns |
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| **Mean estimates:** |
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| * `onetsoc_code` — O*NET occupation code |
| * `task_id` — O*NET task identifier |
| * `mean` — Estimated value (μ or π) |
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| **Full estimates:** |
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| * `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 |
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| O*NET task frequency data is based on: |
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| * **Incumbents (survey respondents)** |
| Provide frequency distributions with associated standard errors |
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| * **Analysts (occupation experts)** |
| Provide point estimates without measures of dispersion |
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| This dataset distinguishes between: |
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| * combined estimates (mean only) |
| * incumbent-based estimates (mean + variance) |
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| ## Coverage |
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| The dataset includes multiple O*NET releases (from version 20.1 onward). |
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| Each version may differ due to: |
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| * updates to task definitions |
| * changes in occupation coverage |
| * new survey responses |
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| ⚠️ **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 |
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| ```python |
| import pandas as pd |
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| 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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| --- |
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| ## Example Applications |
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| * AI exposure measurement at the task level |
| * Workforce decomposition into task bundles |
| * Construction of task-based production functions |
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| ## Limitations |
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| * 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 |
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| If you use this dataset, please cite: |
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| 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. |
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| SSRN: https://ssrn.com/abstract=6174538 |
| DOI: http://dx.doi.org/10.2139/ssrn.6174538 |
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| ## License |
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| 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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