Informal economy and spatial mobility: are informal workers economic refugees?
Bibliographic record
Abstract
Purpose – The paper aims to focus the attention on a particular segment of the labor market – informal workers. Despite a large literature on migration, interesting and relevant questions remain to be studied. The paper investigates whether informal workers could be compared to political refugees in terms of their performance in the source and in the destination economies. The paper estimates the effects of wage differentials, education and other personal and labor market controls on the probability of migration. Design/methodology/approach – The paper studies empirically the probability of migration of workers engaged in informal activities in Brazil using a binary choice model (probit) with particular attention to the self-selection problem of migrants. The paper uses data from the Informal Urban Economy Survey (IBGE). Findings – The results show that the probability of migration of informal workers is negatively related to a worker's education level. The paper finds that the probability of migration is increasing in the ability bias and in wage differentials. The results bring new evidence regarding the possibility of negative selection of migrants considering their observable characteristics, while it corroborates a positive selection of ability or unobservable characteristics of informal worker migrants. The paper presents evidence that less-educated workers are more likely to migrate and show that informal workers migrants behave as economic refugees. Originality/value – To the best of the authors' knowledge, this is the first paper to study the migration of workers engaged in informal activities.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".