The “Peter Pan Syndrome” in Emerging Markets: The Productivity-Transparency Trade-off in IT Adoption
Bibliographic record
Abstract
Firms invest in technology to increase productivity. Yet in emerging markets, where a culture of informality is widespread, information technology (IT) investments leading to greater transparency can impose a cost through higher taxes and the need for regulatory compliance. The tendency of firms to avoid productivity-enhancing technologies and remain small to avoid transparency has been dubbed the “Peter Pan Syndrome.” We examine whether firms make the trade-off between productivity and transparency by examining IT adoption in the Indian retail sector. We find that computer technology adoption is lower when firms are motivated to avoid transparency. Specifically, technology adoption is lower when there is greater corruption, but higher when there is better enforcement and auditing. So, firms have a higher productivity gain threshold to adopt computers in corrupt business environments that suffer from patchy and variable enforcement of the tax laws. Not accounting for this motivation to hide from the formal sector underestimates productivity gains from computer adoption. Thus, in addition to their direct effects on the economy, enforcement, auditing, and corruption can have indirect effects through their negative impact on adoption of productivity-enhancing technologies that also increase operational transparency.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".