Dropout and Enrollment Trends in the Post-War Period: What Went Wrong in the 1970s?
Bibliographic record
Abstract
Over most of the 20 th century successive generations of U.S. children had higher enrollment rates and rising levels of completed education. This trend reversed with the baby boom cohorts who attended school in the 1970s, and only resumed in the mid-1980s. Even today, the college entry rate of male high school seniors is not much higher than it was in 1968. In this paper, we use a variety of data sources to address the question "What went wrong in the 1970s?" We focus on both demand-side factors and on a particular supply-side variable -the relative size of the cohort currently in school. We find that tuition costs and local unemployment rates affect schooling decisions, although neither variable explains recent trends in enrollment or completed education. We also find that larger cohorts have lower schooling attainment, and that aggregate enrollment rates are correlated with changes in the earnings gains associated with a college degree. For women, our results suggest that the slowdown in education in the 1970s was a temporary response to large cohort sizes and low returns to education. For men, however, the decline in enrollment rates in the 1970s and slow recovery in the 1980s point to a permanent shift in the inter-cohort trend in educational attainment that will affect U.S. economic growth and trends in inequality for many decades to come.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".