Median labor income in Chile revised: Insights from Distributional National Accounts
Bibliographic record
Abstract
Abstract: A commonly used figure to highlight inequality in Chile is the median income of the Chilean socioeconomic household survey (known by its acronym in Spanish, CASEN). According to this survey, in 2017 the median monthly income per worker was CLP (Chilean pesos) 400,718 pesos, which compares to average income per worker from National Accounts of CLP 1,350,000 in the same year. For this difference to be correct, the implied Gini coefficient would be 0.7, which much above the Gini implied by the same survey. However, surveys, such as CASEN, often underreport income, particularly for middle- and high-income earners, leading to an underestimation of the median income. This study compares various data sources, including national accounts, household surveys, and administrative records, to create a more accurate picture of income distribution and median income. The corrected data shows higher median incomes and greater inequality than previously reported. On average, the underestimation of gross wages in the Chilean national household survey as compared to national accounts is 40%, significantly larger than other countries. About a quarter of this gap is attributed to the “missing rich” in the survey. For 2017, this equates to an estimated median gross income for dependent labor of CLP 600,000 and CLP 570,000 for all workers. The corrected mean-median income ratio (Gini) is 26% (17%) larger than in the raw survey of 2017 and falls only 6% (3%) between 2006 and 2017 compared with a larger decline of 12% (11%) in the original data.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".