The elusive quest for balanced regional growth from Barlow to Brexit: Lessons from partitioning regional employment growth in Great Britain
Bibliographic record
Abstract
Abstract The British Government’s economic strategy for post‐Brexit Britain of achieving balanced regional growth by “driving growth across the whole country” echoes the objectives set by the Barlow Report of 1940. The regional policies that followed the Barlow Report were heavily influenced by papers written for the Commission by G D A (later Sir Donald) MacDougall. The first of these papers was included as an appendix to the report itself and introduced the shift‐share methodology to the analysis of regional employment growth, and subsequently shown to be flawed. The second paper considered the urban hierarchy and growth but was never fully developed. Consequently post‐war regional policy focussed on the contribution of industrial structure to employment growth without fully taking into account the urban hierarchy or regional locations of that employment. This article replaces the flawed shift‐share methodology with multifactor partitioning (MFP) and applies it to regional employment growth for the period 1971‐2012, a span of special interest because it largely coincides with British membership of the European Union (EU). The deficiencies in the second paper are addressed by introducing allometry to measure the employment growth of each region relative to that of Great Britain and then regression analysis to relate the allometries to distance from London. The results of the two sets of analyses highlight the need for a multiple‐factor, comprehensive, and integrated approach to regional policy and provide a benchmark against which to gauge the success of Britain's post‐Brexit policy of driving future growth across the whole country.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".