The Decomposition of Productivity Growth for India: Before and After 1991
Bibliographic record
Abstract
We analyse how structural shifts affected India’s productivity growth from 1983 to 2017. The analysis is carried out within a broad three-sector level of disaggregation of the aggregate economy. We show that about 29% of the aggregate productivity growth of India during this period comes from structural change. While this contribution has declined since 2007, the remaining 71% of the productivity growth originates within the individual sectors, especially within the tertiary sector, which alone explains about 30% of this productivity growth. About a quarter of the total productivity growth that occurred prior to economic liberalization in 1991 is attributed to the tertiary sector. We found that between 2007 and 2017, the tertiary sector was responsible for almost 38% of India’s total productivity growth. This contribution is much larger than the contribution made by structural change, and, therefore, the significance of the tertiary sector in Indian economic expansion is growing. Our findings also indicate that the most structural change occurred between 1998 and 2007, which may have contributed to productivity increase in the decade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".