Impact of technical efficiency and input-driven growth in the Indian food processing sector
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG) Design/methodology/approach This study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function. Findings The findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes. Research limitations/implications This study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies. Originality/value Previous research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it