A Common Framework to Analyze Social Mobility and Inequality of Opportunity. An Application to the Core and Peripheral Areas of Chile, Colombia, and Mexico
Bibliographic record
Abstract
This paper presents a unified framework to analyze social mobility (SM) and inequality of opportunity (IOp) in Chile, Colombia, and Mexico, focusing on subnational disparities in intermediate functional areas. Using data from the 2018 Household Survey on Territorial Dynamics and Wellbeing, we estimate SM and IOp simultaneously to investigate how individuals’ origins and circumstances influence their economic outcomes as adults. The study employs rank-rank regression to measure relative SM and introduces additional variables, such as sex and territorial characteristics, to capture IOp. Our findings indicate that absolute mobility is similar across the three countries, but relative mobility is higher in Chile, while IOp is lower compared to Colombia and Mexico. Parental wealth is the most significant determinant of IOp in Mexico, whereas territorial factors play a more influential role in Chile and Colombia. The results suggest that policies aimed at reducing IOp and enhancing SM should combine place-based and person-based interventions, especially in countries where territorial characteristics significantly impact socioeconomic outcomes. This paper contributes to the literature by offering a comparative analysis of SM and IOp within a common analytical framework, thereby enhancing our understanding of the complex interactions between these two concepts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".