The Use and Impact of Job Search Procedures by Migrant Workers in China
Bibliographic record
Abstract
Job search procedures are a form of human capital investment in that they involve current investments to enhance future returns, analogous to human capital investments in areas such as education, training and mobility that yield future returns. While the theoretical and empirical literature on job search is extensive, most of it involves developed countries. There is less on developing countries and very little on China involving migrant workers in spite of their growing practical and policy importance and the fact that they are constantly engaging in job search. This paper examines the use and impact of job search procedures used by migrant workers in China by taking advantage of a rich data set on migrant workers that has information on their job search procedure as well as a wide array of other personal and human capital characteristics. Our OLS estimates indicate that there is no effect on earnings of using informal versus formal job search procedures for migrant workers in China. However, our IV results suggest that the OLS estimates are subject to severe selection bias from the fact that the choice of job search procedure is endogenous, associated with unobservable factors that affect the choice of informal versus formal procedures and that affect the earnings outcome. Our three different IV estimates designed to deal with this bias indicate that informal procedures (various aspects of family and friends) are associated with earnings that are 33 to 43% below the uses of more formal procedures. The decomposition results indicate that the most important variable contributing to pay advantage of those who use formal as opposed to informal procedures is education. In sum, our results suggest that policies to encourage or facilitate migrant workers using more formal job search procedures and reducing barriers that compel them to rely on informal procedures can yield better job matches with higher earnings.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".