Life After the High-tech Downturn: Permanent Layoffs and Earnings Losses of Displaced Workers
Bibliographic record
Abstract
The high-tech sector was a major driving force behind the Canadian economic recovery of the late 1990s. It is well known that the tide began to turn quite suddenly in 2001 when sector-wide employment and earnings halted this upward trend, despite continued gains in the rest of the economy. As informative as employment and earnings statistics may be, they do not paint a complete picture of the severity of the high-tech meltdown. A decline in employment may result from reduced hiring and natural attrition, as opposed to layoffs, while a decline in earnings among high-tech workers says little about the fortunes of laid-off workers who did not regain employment in the high-tech sector. In this study, I use a unique administrative data source to address both of these gaps in our knowledge of the high-tech meltdown. Specifically, the study explores permanent layoffs in the high-tech sector, as well as earnings losses of laid-off high-tech workers. The findings suggest that the high-tech meltdown resulted in a sudden and dramatic increase in the probability of experiencing a permanent layoff, which more than quadrupled in the manufacturing sector from 2000 to 2001. Ottawa-Gatineau workers in the industry were hit particularly hard on this front, as the permanent layoff rate rose by a factor of 11 from 2000 to 2001. Moreover, laid-off manufacturing high-tech workers who found a new job saw a very steep decline in earnings. This decline in earnings was well above the declines registered among any other groups of laid-off workers, including workers who were laid off during the "jobless recovery" of the 1990s. Among laid-off high-tech workers who found a new job, about four out of five did not locate employment in high-tech, and about one out of three moved to another city. In Ottawa-Gatineau, many former high-tech employees found jobs in the federal government. However, about two in five laid-off high-tech workers left the city.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".