Early and Late Human Capital Investments, Borrowing Constraints, and the Family
Bibliographic record
Abstract
This paper investigates the importance of family borrowing constraints in determining human capital investments in children at early and late ages. We begin by providing new evidence from the Children of the NLSY (CNLSY) which suggests that borrowing constraints bind for at least some families with young children. Next, we develop an intergenerational model of lifecycle human capital accumulation to study the role of early versus late investments in children when credit markets are imperfect. We analytically establish the importance of dynamic complementarity in investment for the qualitative nature of investment responses to income and policy changes. We extend the framework to incorporate dynasties and use data from the CNLSY to calibrate the model. Our benchmark steady state suggests that roughly half of young parents and 12% of old parents are borrowing constrained, while older children are unconstrained. We also identify strong complementarity between early and late investments, suggesting that policies targeted to one stage of development tend to have similar effects on investment in both stages. We use this calibrated model to study the effects of education subsidies, loans and transfers offered at different ages on early and late human capital investments and subsequent earnings in the short-run and long-run. A key lesson is that the interaction between dynamic complementarity and early borrowing constraints means that early interventions tend to be more successful than later interventions at improving human capital outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".