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Record W2936491607 · doi:10.34989/san-2017-20

Digitalization and Inflation: A Review of the Literature

2021· review· en· W2936491607 on OpenAlex
Karyne B. Charbonneau, Alexa Evans, Subrata Sarker, Lena Suchanek

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueStaff Analytical Notes · 2021
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsBank of Canada
Fundersnot available
KeywordsInflation (cosmology)EconomicsKeynesian economicsMonetary economicsMacroeconomicsPhysics

Abstract

fetched live from OpenAlex

In the past few years, many have postulated that the possible disinflationary effects of digitalization could explain the subdued inflation in advanced economies. In this note, we review the evidence found in the literature. We look at three main channels. First, we find that changes in the prices of information and communication technology-related goods and services included in the CPI have had a negligible effect on inflation in Canada. Second, we find that, due to the small share of e-commerce in Canada and the remarkably similar behaviour of online and offline prices, the “Amazon effect” has only had a small disinflationary impact to date. As e-commerce grows, however, downward pressure on inflation may amplify in the future through increased competition, but digitalization may also increase market concentration. Finally, although cost-efficient technologies should lead to increased productivity, which would put downward pressure on inflation, this effect has yet to appear in the statistics. Overall, we find it unlikely that digitalization has so far had a significant effect on inflation in Canada.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.274
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it