Information technology substitution revisited
Bibliographic record
Abstract
Taking advantage of the opportunities created by the price adjusted performance improvement in IT depends in part on the ability of IT capital to substitute for other inputs in production. Studies in the IS literature as well as most economics training that examine substitution of IT capital for other inputs use the Allen elasticity of substitution (AES). We present a less-well-known measure for the elasticity of substitution, the Morishima Elasticity of Substitution (MES). In contrast to the AES which is misleading when there are three or more inputs – such as non-IT capital, labor and IT capital – the MES provides a substitution measure where the scale is meaningful, and the measure differs depending upon which price is changing. This is particularly important for IT capital as prices have been declining and there is evidence that IT capital can substitute for non-IT capital or labor in a qualitatively different way than non-IT capital and labor substitute for each other. Methodologically we also show the impact of imposing local regularity – for example, monotonicity of output from increases in inputs – that we do through Bayesian methods employed to estimate the underlying functions that are used to calculate various measures of substitution. We demonstrate the importance of the MES as an under-recognized measure of substitution and the impact of imposing local regularity using an economy-wide industry-level dataset covering 1998-2009 at the three-digit NAICS level. Our MES results show that reductions in the price of IT capital increase the quantity of IT capital in use but are unlikely to change the input share of IT capital – the value of IT capital as a proportion of the value of all inputs, in contrast to major studies using the AES. In addition, estimates for both elasticities of substitution are more stable after imposing local regularity. Both of these advances – that is, the MES and imposing local regularity – have potential to impact future work on IT productivity, IT pricing, IT cost estimation and any type of analysis that posits the substitution of IT capital for non-IT capital or labor.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".