El Consumo de las Familias en Ecuador: Incidencia de las Remesas, Recaudación Tributaria y la Inversión Público-Privada mediante MVAR, 2000-2023
Bibliographic record
Abstract
This study analyzes the impact of remittances, tax collection, and public-private investment on the consumption of Ecuadorian families during the period 2000-2023, using a Vector Autoregression (VAR) model to determine the causal relationships and dynamic effects between these variables. The main objective was to evaluate how variations in these variables affect private consumption, which represents more than 64% of aggregate demand in Ecuador. The methodology included logarithmic transformation of time series, stationarity tests, selection of optimal lags, and the application of impulse-response functions to measure short- and medium-term impacts. The results indicate that remittances and investment have a positive but temporary impact on consumption, with significant effects in the first quarter after a shock (0.24% and 0.15%, respectively). On the other hand, tax collection shows a positive initial effect (0.16%), but in the medium term it exerts fiscal pressure, reducing consumption by 0.15% in the sixth quarter. These conclusions highlight the importance of economic policies that promote sustainable investment and balance tax collection with household well-being, contributing to economic growth and social stability in Ecuador.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".