Interactive effects between input and output technical inefficiencies
Bibliographic record
Abstract
Abstract This paper derives a new set of results that provide corrective measures of overall technical inefficiency that either have been ignored or wrongly assumed in the literature. Using directional distance functions, we argue that overall technical inefficiency is not only a function of input and output technical inefficiencies as previous studies claim but also of the interaction between them. The derivation of the interactive effects between input and output technical inefficiencies (IEIOs) solves the arbitrary decomposition of overall technical inefficiency into input and output components. We also show that the IEIO depends on the choice of the directional vector and whether quantities and prices are taken into consideration. Using exogenous and endogenous directional vectors, we prove these results theoretically and empirically using the US commercial banking data set. Using Bayesian estimation with the monotonicity conditions imposed at each observation, we estimate input and output technical inefficiencies separately using directional input and output distance functions with the three commonly used directional vectors; the unit value, the observed input−output, and the optimal directional vectors. The overall technical inefficiency is estimated using systems of equations to incorporate the interactive effect equation and to address the endogeneity of inputs and outputs. Consistent with the theoretical results, we find significant evidence of the IEIO which has a negative effect on the overall technical inefficiency. This result is robust to alternative directional vectors and model specifications, suggesting that the adjustability of both inputs and outputs is required for the improvement of the efficiency of the US commercial banks.
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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".