Bibliographic record
Abstract
This dissertation consists of four chapters on topics in labor and development economics. Chapter 1 discusses innovation fluctuations in an aging economy. A three-stage overlapping generations model is constructed to simulate population trends and their consequent impact on innovation and economic development. Both the theoretical analysis and empirical verification show that countries with a low fertility rate have a higher innovation rate in a short period but a lower one in the long run. This chapter discusses the negative impact of an aging economy and yields the strong policy implication of providing a subsidy to boost the fertility rate, particularly for those developed European countries suffering from aging problems. Chapter 2 focuses on the relationship between the unemployment rate, working efficiency, and working overtime. It is motivated by the widely observed phenomenon of working overtime in some East Asian countries as well as the consulting and investment banking sectors. Does working overtime indeed produce extra efficiency? I use data from the Panel Study of Income Dynamics (PSID) to answer this question. The answer is NO, and it has little impact on the unemployment rate. This chapter contributes to existing literature with a new empirical approach. It could also guide corporate management professionals in the enactment of proper overtime work policies. Chapter 3 discusses the relationship between institutions and economic outcomes. The renowned MIT and Chicago political economists Daron Acemoglu and James Robinson argue that only inclusive political and economic institutions could lead to economic prosperity. Others dispute that their theory captures the whole picture. Bill Gates notably said that the theory of Acemoglu and Robinson must take into account other important factors. Chapter 3 contributes to this controversy. I find that economic prosperity could be harvested even by extractive institutions with immigrant-friendly policies. For example, countries like the United Arab Emirates, Qatar, and Singapore could enjoy a long-term economic boom due to a good immigration policy that attracts high-skilled and low-skilled immigrants from their neighbors. Chapter 4 intends to determine the empirical relationship between income inequality and consumption inequality in China. Existing empirical literature shows that consumption inequality exceeds income inequality in China, which contradicts basic economic theory as well as evidence from the US, Canada, and Europe. I use the inverse relationship between necessity good consumption and income to derive the true income level of each individual. High-income communist party members and employees of government-related agencies tend to hide their grey income, and income inequality is thus underestimated. The relationship I uncover between income and consumption inequality is consistent with the empirical evidence on other countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".