Shift‐Share Analysis and Multifactor Partitioning: What do Aggregated Data Hide?
Bibliographic record
Abstract
ABSTRACT Shift‐share analysis (SSA) is a widely used tool for studying economic changes, particularly in employment, due to its simplicity and minimal data requirements. However, its reliance on crude growth rates and issues associated with aggregation can lead to biases, such as Simpson's Paradox, that may hide regional and industry‐specific insights. Multifactor Partitioning (MFP) addresses these limitations by standardizing growth rates in a way that disentangles industry and regional effects. This paper compares SSA and MFP using employment data from 10 U.S. states between 2005 and 2019. The analysis incorporates three levels of disaggregation: (1) aggregate employment and time, (2) disaggregated employment with aggregated time, and (3) both sectoral and temporal disaggregation. Results show that while SSA and MFP yield similar conclusions at an aggregate level, discrepancies emerge in disaggregated analyses, particularly in high‐growth regions. These findings highlight the importance of data disaggregation and MFP's capacity to provide nuanced insights for policymakers and researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".