Total Factor Productivity Growth in the G7 Countries: Different or Alike?
Bibliographic record
Abstract
The paper compares the contribution of total factor productivity (TFP) to economic growth in the G7 countries, from 1960 until 2005. A dynamic world translog stochastic production frontier is computed through Bayesian statistical methods using panel data on 21 OECD economies. The real GDP growth rate is decomposed in TFP and input accumulation contributions', the former being divided in two components: efficiency developments (the distance to the world production function) and technological progress (the expansion of the world production function). The paper adopts the methodology suggested by Koop, Osiewalsky and Steel (1999), though it covers a much larger period, allowing for the identification of intertemporal growth patters. The growth accounting exercise requires a Gibbs Sampling iteration algorithm and it is carried out for eight periods, each one covering ten yearly growth rates, with overlapping sub periods of five years. The results obtained show that the contribution of technological progress to total TFP is typically stronger than efficiency improvements. The US and Canada recorded a TFP acceleration after the mid 1980s, following declines in the previous decades. In addition, the inputs accumulation gave a relatively stable contribution for GDP growth throughout the sample period. Italy and France present a continuous declining trend in TFP contribution, though more marked in the latter case. Germany and the UK seem to have moved to a new lower floor of TFP contribution in the last decades. Japan, presents a downward trend in TFP contribution that is even more pronounced than in Italy. However, some reversal was seen in the Japanese TFP in the last decade considered. The shape of the stochastic production function changed along the period considered, benefiting more capital intensive input-combinations. In addition, there is some evidence of increasing returns to scale in the G7 countries, though it may be related with the non consideration of quality aspects in the measurement of inputs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".