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Record W4403089964 · doi:10.15353/rea.v14i2.5008

The Ability to Work Remotely: Measures and Implications

2022· article· en· W4403089964 on OpenAlexvenueno aff
Kathryn Langemeier, Maria D. Tito

Bibliographic record

VenueReview of Economic Analysis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Computer scienceEngineering

Abstract

fetched live from OpenAlex

Our paper explores how the ability to work remotely has changed over time, its relationship with demographic characteristics and employment outcomes, and the role it played during the pandemic recession. We focus on two different remote work indexes, a measure of “no physical presence”—proposed by Dingel and Neiman (2020)—and a measure of “remote communications”—presented by Montenovo et al. (2020). While the two measures suggest a similar prevalence of remote work in recent years and display fairly similar behaviors across demographic groups and in terms of wage and employment outcomes, their evolution is significantly different. While the share of occupations that require no physical presence has remained relatively constant since July 2003, the share of those occupations featuring remote communications increased more than 40 percentage points over the same period. Those differing evolutions paint a starkly different picture of the role of remote work during the pandemic: Compared to a counterfactual scenario that keeps the remote classification constant at the 2004 levels for each measure, the index of no physical presence suggests that there would have been little changes in terms of aggregate hours losses during the pandemic, while the index of remote communications points to much larger declines in aggregate hours. Even without much change in the ability of working remotely, the trend towards more remote work underscores the importance of the hours margin as an additional dimension for the realization of gains from working at home.

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 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)0.002
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.059
GPT teacher head0.395
Teacher spread0.336 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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