Job tasks and the comparative structure of income and employment: Routine task intensity and offshorability for the LIS*
Bibliographic record
Abstract
Comparative sociologists have long considered occupations to be a key source of inequality. However, data constraints make comparative research on two of the more important contemporary drivers of occupational stratification – globalization and technological change – relatively scarce. This article introduces a new dataset on occupational ‘routine task intensity’ (RTI) and ‘offshorability’ (OFFS) for use with the Luxembourg Income Study (LIS). To produce these data, we recoded 23 country-specific occupational schemes (74 LIS country-years) to the two-digit ISCO-88 scheme. When combined with the handful of LIS countries already reporting their occupations in ISCO-88, we produce individual level RTI and OFFS scores for 38 LIS countries and 160 LIS country-years. To assess the validity of these recodes, we compare average labor-income ratios predicted by recoded ISCO-88 occupational categories to those predicted by reported ISCO-88 occupational categories within countries that transitioned from country-specific to ISCO-88 codes over time. To assess the utility of these RTI and OFFS scores and advance the literature on income polarization, we analyze their association with work hours and labor incomes in the global North and South. Both covariates correlate with work hours in ways that are consistent with previous research and additional theoretical considerations. Moreover, we show that both RTI and OFFS contribute to income polarization directly in the North, but not in the South. This article generates a public good data infrastructure that will be of use to a wide variety of social scientists, and brings new evidence to bear on the question of income polarization in rich democracies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".