Bibliographic record
Abstract
This dissertation studies the role of informal producers in determining the pattern of production and the trade structure of developing countries, and provides an understanding of the channels through which informal producers influence the outcome of trade. The second chapter examines the characteristics of informal workers in the Turkish export-oriented manufacturing industries using descriptive statistics and econometric analysis. We find that informality is highest among female workers with lower education levels who mainly operate from home, and report that the textile and clothing industries, which Turkey has a comparative advantage in the world market, have a share of informality. This finding inspires the third and fourth chapters. In the third chapter, informal producers directly export their products in the global market. We build a Ricardian model of trade with labour market frictions that justify the presence of informal producers. We find that the size of the informal sector is affected by not only a country's own labour market structure, but also by its trading partners' labour market frictions. We show that a decrease in trade costs results in an increase in the size of the informal sector in countries with relatively higher labour market rigidities, and a reduction of informality in their trading partners. The fourth chapter, which is joint work with professor Claustre Bajona, develops a theoretical framework with stages of production to investigate the role of informal producers in the supply chain. To quantify the changes in labour and trade policies on the trade structure of countries, we calibrate the model to the Turkish and German economies in 2003. In the model, informal producers may influence the comparative advantage of countries by supplying lower cost inputs to formal producers that compete in the global arena. In our calibrated model, second stage formal production is higher under a protectionist economy in a country with higher labour market frictions. This results in a larger informal sector due to the interlinkage between formal and informal producers in the production chain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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; 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".