Good Names Beget Favors: The Impact of Country Image on Trade Flows and Welfare
Bibliographic record
Abstract
This paper estimates the effects of time-varying consumer preference bias on trade flows and welfare. We use a unique data set from the BBC World Service Poll, which surveys (annually during 2005–2017 with some gaps) the populations of a wide array of countries on their views of whether an evaluated country is having a mainly positive or negative influence in the world. We identify the effects on consumer preference parameters due to shifts in these country image perceptions and quantify their general equilibrium effects on bilateral exports and welfare (each time for an evaluated exporting country, holding the exporting country’s own preference parameters constant). We consider five important shifts in country image: the George W. Bush effect, the Donald Trump effect, the Senkaku Islands Dispute effect, the Brexit effect, and the Good-Boy Canadian effect. We find that such changes in bilateral country image perceptions have quantitatively important trade and welfare effects. The negative impact of Donald Trump’s “America First” campaign rhetoric on the U.S.’s country image might have cost the United States 4%–5% of its total exports and welfare gains from trade. In contrast, the consistent improvement of Canada’s country image between 2010 and 2017 has amounted to more than 8% of its total welfare gains from trade. This paper was accepted by Matthew Shum, marketing.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".