Informal work and how to measure it: A formal consensus at the 100th International Conference of Labour Statisticians
Bibliographic record
Abstract
Over the past fifty years, interest in and analysis of informality at work has burgeoned. Informal employment, including but not limited to un-declared work, is a core concern for unions worldwide. In 2019, nearly 2 billion workers (about 6 in every 10) were in informal employment. Informal employment is found in all countries, but its prevalence is inversely proportional to income being highest in low income countries at around 90 per cent of total employment, and lowest in high-income countries at less than 20 per cent of total employment. The share of women in informal employment exceeds that of men in most countries.1 Statistics on informal employment are vital for describing the structure and extent of informal employment. They are essential to identify groups of persons in employment most represented and at risk of informality, and to provide information on exposure to economic and personal risks, decent work deficits and working conditions. For unionists and policy makers, there is a need to measure the prevalence of informality across jobs, economic units and activities; the distribution of informal and formal jobs by socio-demographic characteristics; the percentage of persons with informal main jobs in the informal and formal sectors; levels of protection for those in informal and formal employment; and contextual vulnerabilities, including poverty, inequalities, discrimination, access to land and natural resources, household composition, access to social protection. These data provide the evidentiary base to push for and implement policies that can improve the working lives of those in informal employment. (...)
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 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.050 | 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; both teacher heads agree on what is shown here.
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".