Bibliographic record
Abstract
Poor labor productivity may turn UK manufacturing companies into easy targets for foreign buyers. Modern management techniques could be the answer. The decline of Britain's manufacturing sector is not in dispute, though the causes of that decline have prompted much debate. The popular candidates are the general shift of manufacturing toward lower-cost developing countries and the recent high value of the pound sterling. But while such exogenous factors doubtless play a part, the troubles of the UK manufacturing sector result in large measure from a factor that, fortunately, lies squarely within its managers' control: labor productivity. The proof lies in the productivity of foreign-owned plants in the United Kingdom. In US-owned plants there, for example, labor is about 80 percent more productive on average than it is in UK-owned plants--in every sector. As a result, overall productivity is suffering and so, consequently, are both UK manufacturing's contribution to the country's gross domestic product and the financial performance of individual manufacturers. While it is true that--with the exception of the United States--developed nations generally have lost trade in manufactured goods to developing markets over the past ten years, manufacturing's share of GOP fell faster in the United Kingdom than in any other Group of Seven (G-7) country, reaching just 17.7 percent at the end of 1999. This shrinkage isn't explained by extraordinary growth in the United Kingdom's nonmanufacturing sectors, whose average annual gain in output from 1989 to 1999 was 2 percent a year. By contrast, manufacturing's average annual growth over the same period was only 0.4 percent. In Canada, France, and the United States, manufacturing has actually gained share in recent years; in Germany, Italy, and Japan, the losses have been smaller than those in the United Kingdom. Some argue that the relatively weak recent growth of the UK manufacturing sector is attributable in part to its dependence on faltering old-economy industries such as food processing, paper, and textiles. In the United States, by contrast, the faster-growing electronics and mechanical-engineering industries, which support the new economy, dominate manufacturing. But even after the disparity between the sector mix in the United Kingdom and the United States is factored out, figures for the period from 1995 to 1999 show that the US manufacturing sector still grew at a rate of almost 4 percent a year while its UK counterpart notched growth of just half a percent. On the financial front, UK manufacturing has destroyed more than [pound]80 billion ($110 billion) in value over the past ten years, and the already substantial difference between the UK and the US manufacturing sectors' net rate of return appears to be widening (Exhibit 1). So too does the gap in total productivity between manufacturing in the United Kingdom, on the one hand, and in Canada, France, Germany, Italy, Japan, and the United States, on the other. Total US productivity, for example, which was 29 percent greater than the United Kingdom's in the period from 1994 to 1996, had become 38 percent greater by 1998. Many observers suggest that an increase in capital investment will bridge the gap. A close look at the figures, however, suggests that the level of capital investment is not the problem, nor is raising it the solution. The United Kingdom's rate of capital expenditure has been growing, and at 14 percent of the value of output in 1998 it now matches the levels of the country's strongest competitors: Germany, with 15 percent, and the United States, with 13.6 percent. Capital intensity -- the ratio between the contributions of capital (the numerator) and labor (the denominator) to production--remains low in the UK manufacturing sector because of its previously low capital spending. But while the United Kingdom's capital stock is catching up--albeit from a low base--there are diminishing returns to further capital investment in terms of labor productivity. …
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.099 | 0.037 |
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".