The Job Ladder
Bibliographic record
Abstract
Abstract Using a range of countries from the Global South, this book examines heterogeneity within informal work by applying a common conceptual framework and empirical methodology. The country studies use panel data to study the dynamics of worker transitions between formal and heterogeneous, informal work. The range of country studies in the book (covering Asia, Latin America, the Middle East, and North Africa and sub-Saharan Africa) allow us to present a comparative perspective across developing countries. Each country study provides a nuanced view of informality, dividing workers into six work status groups: formal wage-employees, upper-tier informal wage-employees, lower-tier informal wage-employees, formal self-employed, upper-tier informal self-employed, and lower-tier informal self-employed. Based on this common conceptual framework, the country studies examine the distribution of workers between each of these work status groups. Using panel data, the country studies document transition patterns across different formality and work status groups. The panel data analysed in each country study gives a basis for making statements about labour market transitions that are not warranted when using comparable cross sections. In addition to measuring the distribution of workers and transitions between work status groups, each country study also examines individual-level and household-level characteristics associated with workers in each work status. Using these characteristics, each country study constructs a ‘job ladder’ that ranks each work status. The country studies then examine the characteristics of workers that are associated with transitions up (and down) the job ladder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 teacher head, 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".