COVID-19 Impacts on the IT Job Market: A Massive Job Ads Analysis
Bibliographic record
Abstract
The Covid-19 pandemic has had a significant impact on many economic sectors. The most severe immediate financial effects of Covid-19 include job losses and decreased hiring, and we can expect generalized lower economic growth in the long term. While such phenomena are there for all to see, measuring their scope is complex. In this work, we performed a massive analysis of job postings (ads) taken from LinkUp, a popular job search web platform, to better understand the occupational trends in IT. We analyzed about nine million ads for computer and mathematical experts to measure the impact of the virus on the IT job market. We also extended our investigations to almost 109 million advertisements (about 300 GB of data) for all kinds of positions to overview the effects of Covid-19 on the job market at large. The results show that the Covid-19 crisis hit the job market during the first two quarters of 2020, causing the number of job advertisements to drop across all sectors (except one). Specifically, the IT sector lost between 15% and 48% of the ads, depending on the specific professional figure. Since the last quarter of 2020, the ad numbers have recovered for some sectors, and by the first 2021 quarter, all of them have more job ads than in the last five years. Finally, we used text analysis to understand the trends of interest in teleworking. We found that in the second quarter of 2020, the number of ads explicitly mentioning telework was almost three times the average of the previous quarters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".