Children of War: The Long-Run Effects of Large-Scale Physical Destruction and Warfare on Children
Bibliographic record
Abstract
During World War II, more than one-half million tons of bombs were dropped in aerial raids on German cities, destroying about one-third of the total housing stock nationwide. This paper provides causal evidence on long-term consequences of large-scale physical destruction on the educational attainment, health status and labor market outcomes of German children. I combine a unique dataset on city-level destruction in Germany caused by Allied Air Forces bombing during WWII with individual survey data from the German Socio-Economic Panel (GSOEP). My identification strategy exploits the plausibly exogenous city-by-cohort variation in the intensity of WWII destruction as a unique quasi-experiment. My findings suggest significant, long-lasting detrimental effects on the human capital formation, health and labor market outcomes of Germans who were at school-age during WWII. First, these children had 0.4 fewer years of schooling on average in adulthood, with those in the most hard-hit cities completing 1.2 fewer years. Second, these children were about half inches (one centimeter) shorter and had lower self-reported health satisfaction in adulthood. Third, their future labor market earnings decreased by 6% on average due to exposure to wartime physical destruction. These results survive using alternative samples and specifications, including controlling for migration. Moreover, a control experiment using older cohorts who were not school-aged during WWII reveals no significant city-specific cohort trends. An important channel for the effect of destruction on educational attainment appears to be the destruction of schools and the absence of teachers, whereas malnutrition and destruction of health facilities during WWII seem to be important for the estimated impact on health. JEL Codes: I21, I12, J24, N34
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".