Informal Work in Latin America: Competing Perspectives and Recent Debates
Bibliographic record
Abstract
Abstract During the ‘lost decade’ of the 1980s, informal work and self‐employment emerged as the most prevalent forms of work throughout Latin America. In response to the economic crisis, the majority of Latin American countries adopted a series of sweeping neoliberal reforms designed to open nations to trade and investment, promote export‐led growth, and generate employment, ultimately reducing the incidence of informal work. Despite the widespread adherence to the neoliberal model and implementation of structural adjustment reforms during the past quarter century, informal work has not diminished and in much of Latin America the odds of finding ‘decent work’ are no better today than during the economic crisis of the 1980s. In light of this seeming paradox, this article offers an overview of the recent debates and controversies surrounding informal work in Latin America. Drawing on recent research, as well as reports and policy documents from key international organizations, I pose and attempt to answer four core questions: What counts as informal work? Who works informally in Latin America? Why do men and women throughout Latin America increasingly resort to informal work? What role does informal work play as a livelihood strategy in Latin America and how has this role changed in recent years?
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 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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".