Remittances: A Small Positive in the Covid Era
Bibliographic record
Abstract
For many low and middle-income countries, remittance payments are a vital contributor to their economy. However, the emergence of the COVID-19 pandemic led to swift action by countries to limit travel and migration, with the hope of protecting their population from the virus. As expected, the initial damage was swift for low and middle-income countries that have a large dependence on remittances for their economy. Due to travel restrictions and industry shutdowns, the remittance market plummeted, with the World Bank predicting a 20% reduction in remittance volume for the entirety of 2020 ( (World Bank, 2020). Unexpectedly, as the overall world economy continued to crash during the second half of 2020, remittances made a major rebound. One of the reasons remittances were able to rebound was due to policy put in place by host countries across the globe (Vlaicu, 2023). This paper aims to analyze the factors that affected remittances over the pandemic in 5 countries: the United States, Canada, Russia, the United Kingdom, and Germany. To do so, this paper analyzes bilateral remittance and immigration data provided by the World Bank as well as daily COVID-19 policy analysis provided by Oxford University. By using regression analysis, the 5 provided remittance host countries are examined to determine what specific policy put in place was the most effective, or a hindrance to, the level of remittances that were being sent during the pandemic. The results from the regression highlight deaths due to COVID and increased immigration levels as being correlated with increased levels of remittances while direct stimulus payments as being negatively correlated. With this information, analyses can be done to examine the reasoning behind these factors and policy decisions to help best prepare the remittance market for future disasters.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".