Empirical essays on human capital: determinants, returns and components
Bibliographic record
Abstract
This thesis is comprised of three empirical essays on the theme of human capital. The essays use natural and laboratory experiments to study the determinants, returns and components of human capital. We first consider the determinants of human capital by studying the effects of maternal care as a determinant of children's human capital. Then we investigate the returns to human capital by studying the effects of immigration policies on immigrants' characteristics and labour market outcomes. Lastly, we examine specific components of human capital through an experiment on non-cognitive skills and preferences. The first essay estimates the causal impact of maternal care on the developmental outcomes of children aged 2-3 years using a parental leave reform implemented in Canada at the end of 2000 as an exogenous variation to maternal care. The reform increased the time mothers spend with their newborns by 3 months without affecting their income net of taxes, transfers and child care costs. Using the Canadian National Longitudinal Survey of Children and Youth, we employ a difference-in-differences methodology to compare children with a sibling born after the reform to those with a sibling born before the reform, relative to children of the same birth cohorts who did not have a younger sibling in the period surrounding the reform. We find that treated children enjoy a 16 percent increase in the time they spend with their mothers, with maternal care crowding out informal care. The increase in maternal care does not translate into better cognitive, non-cognitive or health outcomes in the short-run or the medium-run. The second essay uses a natural experiment to study the effects of a change in the point system, a system that selects immigrants based on specific observable characteristics, on immigrants' characteristics and labor market outcomes. Specifically, in 2001, Quebec changed its point system, by increasing the points for education and French language and decreasing the points for a subjective category "adaptability". The objective of the reform was to increase the number of French-speaking immigrants without deteriorating their labor market performance. Using a difference-in-differences and triple differences methodology, we show that, compared to immigrants to the Rest of Canada, immigrants to Quebec after the reform hold more bachelor's degrees and know more French than immigrants to Quebec before the reform. However, this does not translate into better labor market outcomes. This essay shows how point systems can be used to shape the immigrant workforce according to policy goals. Non-cognitive skills are a recently incorporated component of human capital in the economics literature. In the third essay, we contribute to this literature through a laboratory experiment on personality traits and risk and ambiguity preferences. We also study the effects of personality traits prevalence in a group on the decision making of each group member. In the experiment, subjects reveal their risk and ambiguity preferences through lottery choices. They then participate in an unstructured group chat. Afterwards, they are given the chance to revise their initial lottery choices. Results show that personality traits affect risk and ambiguity preferences before the chat. Specifically, conscientiousness is negatively related to risk and ambiguity aversion and agreeableness is negatively related to ambiguity aversion. We also show that the probability of changing decisions after the chat is affected by the individual's non-cognitive traits but not by the traits of the other group members.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".