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README.md

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 # Measuring the Intensive Margin of Work: Task-Level Labor Input Data
 
-This repository provides task-level measures of labor input within occupations, constructed from O*NET task frequency data.
+This dataset provides **task-level measures of labor input within occupations**, constructed from O*NET task frequency data.
 
-For each job–task pair, we estimate:
+For each occupation–task pair, we estimate:
 
-* **Task flow (μ)**: the expected number of times a task is performed annually within a job
-* **Task share (π)**: the proportion of total labor input allocated to the task
+* **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.
+Under a homogeneous task-duration assumption, task shares can be interpreted as **time allocations across tasks within occupations**.
 
-These measures enable a granular analysis of the internal structure of work and support applications such as AI exposure measurement, workforce decomposition, and automation targeting.
+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
 
 ---
 
-## Citation
 
-If you use this data, please cite:
+## 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.
 
-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)
 
 ---
 
-## Data Description
+## Data Files
 
-Each dataset is defined at the **job–task level** and is derived from O*NET task frequency survey responses.
+Data is organized by:
 
-### Core Measures
+* O*NET version
+* estimate type (mean vs full)
+* measure (flow vs share)
 
-* **Task flow (μ)**
-  Expected annual number of occurrences of a task within an occupation
+### Mean estimates (point estimates only)
 
-* **Task share (π)**
-  Share of total labor input allocated to the task
-  (interpretable as a time share under homogeneous task duration)
+* 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`
 
 ---
 
-## Coverage
+## 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:
 
-Data is provided for multiple O*NET releases (from version 20.1 onward).
-Each version corresponds to a specific O*NET database release and may differ in:
+  * mean (μ or π)
+  * variance (Var)
+
+Full estimates therefore support **statistical inference and uncertainty quantification**, while mean estimates provide broader coverage.
 
-* Task definitions
-* Occupation coverage
-* Survey responses
 
 ---
 
-## Data Files
+## Dataset Structure
 
-### Mean Estimates
+Each dataset is defined at the **occupation–task level**, with one row per `(onetsoc_code, task_id)` pair.
 
-We provide mean estimates for each O*NET version for the following metrics:
+### Columns
 
-#### Task flow (mean estimates)
+**Mean estimates:**
 
-```
-task_labor_input_mean_estimates/{ONET_VERSION}/
-ONET_{ONET_VERSION}_weight_mode_STANDARD_task_flow_mean_estimates.csv
-```
+* `onetsoc_code` — O*NET occupation code
+* `task_id` — O*NET task identifier
+* `mean` — Estimated value (μ or π)
 
-#### Task labor input share (mean estimates)
+**Full estimates:**
 
-```
-task_labor_input_mean_estimates/{ONET_VERSION}/
-ONET_{ONET_VERSION}_weight_mode_STANDARD_task_labor_input_mean_estimates.csv
+* `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
+
+```python
+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"
+)
 ```
 
 ---
 
-## Data Schema
+## Example Applications
 
-Each file contains the following columns:
+* AI exposure measurement at the task level
+* Workforce decomposition into task bundles
+* Construction of task-based production functions
 
-* `onetsoc_code` — O*NET occupation code
-* `task_id` — O*NET task identifier
-* `mean` — Estimated value (flow or share)
+---
+
+## 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
 
-For future releases (full estimators):
+---
+
+## 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.
 
-* `variance` — Estimated variance of the measure
+SSRN: https://ssrn.com/abstract=6174538
+DOI: http://dx.doi.org/10.2139/ssrn.6174538
 
 ---
 
 ## License
 
-This work is licensed under a
-[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
+Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
+(CC BY-NC-ND 4.0)
+https://creativecommons.org/licenses/by-nc-nd/4.0/