Going to College to Avoid the Draft: The Unintended Legacy of the Vietnam War
Bibliographic record
Abstract
Between 1965 and 1975 the enrollment rate of college-age men in the United States rose and then fell abruptly. Many contemporary observers (e.g., James Davis and Kenneth Dolbeare, 1968) attributed the surge in college attendance to draft-avoidance behavior. Under a policy first introduced in the Korean War, the Selective Service issued college deferments to enrolled men that delayed their eligibility for conscription. These deferments provided a strong incentive to remain in school for men who wanted to avoid the draft. For example, the college entry rate of young men rose from 54 percent in 1963 to 62 percent in 1968 (the peak year of the draft). Moreover, both the college entry rate and the number of inductions dropped sharply between 1968 and 1973 as the draft was being phased out. Although these parallel trends are suggestive, they do not necessarily prove that draft avoidance raised the education of men who were at risk of service during the Vietnam War. Such an inference requires an explicit specification of the “counterfactual”: What would have happened to schooling outcomes in the absence of the draft? In this paper we use trends in enrollment and completed schooling of men relative to those of women to measure the effects of draftavoidance behavior during the Vietnam War. Our maintained hypothesis is that, in the absence of gender-specific factors such as the draft, the relative schooling outcomes of men and women from the same cohort would follow a smooth trend. In light of the sharp discontinuity in military induction rates between 1965 and 1970, we look for similar patterns in the relative enrollment rate of men, and in the relative college graduation rate of men from cohorts that were at risk of induction during this period.
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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 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".