Show Me the Money: Four Essays on Remittance Transfer Costs, Channel Modality, and Informal Finance Gaps
Bibliographic record
Abstract
Remittances, defined as funds sent by migrants to recipients in their region of origin or other transnational diaspora members, constitute a substantial portion of global financial flows.Regrettably, there exist sparse estimates regarding the volume and characteristics of informal remittances.If informal channels, such as hand-carried cash across borders, undeclared goods, and other transfers were accounted for, literature suggests that total flows would be substantially larger than currently reported.Policymakers have endorsed various initiatives, namely transfer cost reduction policies, by committing to targets and spurring competition to formalise informal flows.Divided into two main parts, each consisting of two papers or chapters, this dissertation explores topics in remittance behaviour, channel modality (or channel choice), and formalisation policies.In the first part, the dissertation explores the Canadian case study of the micro effects of transfer costs on remittance choices and channel modality using household survey data.As such, the first two papers explore the micro-level factors underlying remittance decision making.The first paper studies the micro-determinants, namely transfer costs, on the propensity to remit and the simultaneous decisions of the amount and frequency with which to remit.The second paper's analysis expands upon Canadian remitters' case study and unpacks the dichotomisation of formal versus informal methods to delve deeper into channel modality at the migrant and household levels.The second part of the dissertation broadens the scope of analysis to a global scale.The third paper delves into policy analysis and international relations theory to analyse the causal pathways that explain how the 2001 9/11 attacks shaped modern formalisation policies.The fourth paper builds upon this policy shift to study the slowdown in global formal remittance growth.Via proxy estimations of the informal sector, results indicate that while formalisation has contributed to the growth in formal remittances, formalisation efforts yield diminishing marginal returns over time.The multi-levelled approach across micro and macro chapters provides a holistic understanding of the gaps in each level of analysis and sheds light on the still liminal literature on remittance modality and formalisation.Orozco, M. (2005).International financial flows and worker remittances: Best practices.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".