Three essays on the informal sector
Bibliographic record
Abstract
This thesis consists of three essays that examine: heterogeneity in informal wage employment; access and use of health insurance by individuals in the informal sector; and the gender wage gap in the sector. The first essay uses data from the sixth round of the Ghana Living Standards Survey (GLSS) and employs a finite mixture model to analyze the structure of informal wage employment, and evidence of segmentation in the Ghanaian labour market. The findings show that Ghanaian informal wage employment comprises two divisions --higher-paid and lower-paid-- each with a distinct wage function. Assuming workers are earnings-maximizers, we find that 72% to 79% of all informal wage employees are involuntary employed in the sector, and would be better off in other divisions of the labour market. In addition, using propensity score matching we estimate that the average informal wage employee, who could maximize their potential earnings in the formal sector, is potentially losing on the average $108.6--$148.7 monthly due to entry barriers in the formal sector. The second essay uses data from the World Bank's Living Standards Measurement Survey (LSMS) and propensity score matching to analyze how voluntary health insurance uptake impacts health care use in Ghana. We find robust evidence that older, wealthier and more educated individuals are more likely to buy health insurance, and that the average individual who voluntarily enrolls in the program uses more health care (about 35%) compared to an identical formal sector worker who is involuntary enrolled. The third essay investigates the gender wage gap across the conditional wage distributions of formal and informal sector wage earners in Ghana using a quantile regression technique with a correction for potential self-selection. In the informal sector, we find a 44% wage gap at the 25th quantile which increases to 52% at the 75th quantile. This contrasts with 5% -10% between same quantiles in the formal sector. This general pattern suggest significantly higher levels of discrimination in the informal sector, implying that the informal sector may be a cause of poverty for some female wage workers employed in the sector.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".