Labor Productivity, Fertility, Development and Antidevelopment in Mexico and Central America
Bibliographic record
Abstract
In El Salvador, due to the reduction of the fertility rate and the loss of population due to emigration, the ratios of female and male employment to population are tending to decrease. This means that the main driver of future development will be labor productivity. The objective of this paper is to identify the variables that determine labor productivity in a panel of data from Mexico, Guatemala, El Salvador, Costa Rica, and the Dominican Republic. Var models are estimated to detect the variables that have importance in the behavior of labor productivity. A distinctive aspect of this work is its emphasis on the role of social variables in labor productivity. Thus, a principal component was estimated with expenditures on health and education as percentages of GDP representing the driving force behind development. Another principal component was estimated to represent the forces favoring anti-development, composed of the linear combination of the number of students per teacher, the homicide rate, the adolescent fertility rate and the female self-employment rate. The first principal component of social expenditures showed a positive association with labor productivity, the economic growth rate and the percentage of students completing high school, and a negative association with adolescent fertility, the percentage of children born with low birth weight, and the mortality rate. The principal component of anti-development showed a negative association with labor productivity and economic growth rate and a positive association with the interest rate on loans, the percentage of low-birth weight children, the female suicide rate and the trade account deficit. The paper ends with the proposal of a series of measures and policies to be implemented so as to increase labor productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".