Leveraging the Power of Place: A Data-Driven Decision Helper to Improve\n the Location Decisions of Economic Immigrants
Bibliographic record
Abstract
A growing number of countries have established programs to attract immigrants\nwho can contribute to their economy. Research suggests that an immigrant's\ninitial arrival location plays a key role in shaping their economic success.\nYet immigrants currently lack access to personalized information that would\nhelp them identify optimal destinations. Instead, they often rely on\navailability heuristics, which can lead to the selection of sub-optimal landing\nlocations, lower earnings, elevated outmigration rates, and concentration in\nthe most well-known locations. To address this issue and counteract the effects\nof cognitive biases and limited information, we propose a data-driven decision\nhelper that draws on behavioral insights, administrative data, and machine\nlearning methods to inform immigrants' location decisions. The decision helper\nprovides personalized location recommendations that reflect immigrants'\npreferences as well as data-driven predictions of the locations where they\nmaximize their expected earnings given their profile. We illustrate the\npotential impact of our approach using backtests conducted with administrative\ndata that links landing data of recent economic immigrants from Canada's\nExpress Entry system with their earnings retrieved from tax records.\nSimulations across various scenarios suggest that providing location\nrecommendations to incoming economic immigrants can increase their initial\nearnings and lead to a mild shift away from the most populous landing\ndestinations. Our approach can be implemented within existing institutional\nstructures at minimal cost, and offers governments an opportunity to harness\ntheir administrative data to improve outcomes for economic immigrants.\n
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 | 0.003 | 0.009 |
| 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 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".