Bibliographic record
Abstract
During the first two thirds of the 20th century, electricity, running water, and a host of new consumer durables diffused into most American homes. These new household technologies revolutionized domestic life by freeing up time from basic housework. In this dissertation, I study the consequences of household technological change on families, focusing on fertility, child health, marriage, and female labour force participation. Chapter 1 provides a short history of household modernization. I then present an econometric framework for evaluating the effects of household technological change, and discuss the main estimation challenges. To address these issues, I introduce an estimation strategy based on a newly-assembled dataset that captures the rollout of the U.S. power grid during the mid-20th century. In chapter 2, I study the impact of household technological change on fertility and child health, exploiting substantial cross-county and cross-state variation in the timing of when households acquired new consumer durables. Modern household technologies led families to make a child quantity-quality tradeoff favouring quality: household modernization is associated with decreases in infant mortality and decreases in fertility. The declines in infant mortality were particularly large in states where households had relied heavily on coal for heating and cooking, where the potential to improve indoor air quality was greatest. Health improvements were also larger in states that had previously invested heavily in maternal education, suggesting that household modernization led parents to provide better infant care. Overall, household technological change can account for between 25% and 30% of the total decline in infant mortality between 1930 and 1960. In chapter 3, I examine the relationship between household modernization, investment in children, and female employment. I present a conceptual framework in which household technological change has little immediate impact on female employment, but generates increased investment in daughters' human capital, ultimately causing a rise in employment for subsequent cohorts of women. I find empirical support for these predictions. Further, the results suggest that the diffusion of modern technology into the home during the first half of the 20th century can account for a significant fraction of the rise in female employment after 1950.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".