Germany’s Continued Productivity Slump: An Industry Analysis
Bibliographic record
Abstract
US productivity growth surged twice post 1995 and post 2000. In contrast Germany registered two successive productivity reductions during that same period of time. Previous analysis of the post-2000 decline has been limited, however, by the short time series of the available data. In this paper we extend the Ifo Industry Growth Accounting Database that provides detailed industry-level investment information up to 2004. While much attention has focused on the reduction in German labor hours, our post-2000 data shows that a fledgling recovery in German non-ICT investment was offset by a widespread collapse in German total factor productivity. Almost half of German industries (accounting for over 45 percent of German output) did not experience positive TFP growth post 2000. Industries that constitute over a quarter of Germanys value-added exhibited negative labor productivity growth during the same period. The negative German productivity trend is thus continuing, which accelerates the countrys departure from the productivity frontier.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".