Spatial Interaction Regional Model for the Mexican Economy (SIRMME): A Special Case for Mexico City Metropolitan Area
Bibliographic record
Abstract
This paper analyzes empirically a macro model and a regional model to explain Mexico and Mexico City economies respectively. Typically, regional economic modeling considers either a top-down or bottom-up approach to model regional difference in economic growth. This paper shows results that explain regional difference in Mexico from the bottom-up through a special case that focuses on the spatial interaction between Mexico City -the main economic engine of Mexico- and the rest of the country during the period 2000-2010. Our results indicate that variables associated with human capital, internal migration, "creative class", micro-firms and spatial interaction among micro-regions were conditioning the differential growth between Mexico City and the whole country during the period 2000-2010. Likewise, we present econometric results of a typical macro model that explains economic growth in Mexico by different income effects on components of aggregate demand during the period 1993-2010. The purpose of both exercises is to motivate future research for the Mexican case to link macro components (such as export driven forces, Mexico´s dependency to the USA´s business cycle, loss of government spending, etc.) with their local counterparts such as agglomeration economies, human and creative capital stock, regional spillovers, natural resources, dynamic population, etc. to explain regional differential growth.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".