The role of information about the tradeoffs of offshoring and AI on attitudes towards these economic shocks: A conjoint experiment
Bibliographic record
Abstract
Automation and artificial intelligence (AAI) and offshoring can engender both negative impacts, such as job displacement, and positive outcomes, including increased productivity and lower prices. However, the public's perception and political consequences of these impacts remain uncertain. While economists emphasize that increased automation has been a significant driver of job loss, the political discourse is more inclined to attribute job displacement to trade. As a result, trade has garnered greater attention in political discussions on job loss, while AAI remains less politicized. How do citizens evaluate the trade-offs associated with these economic shocks? Are they equally concerned about economic changes arising from offshoring as opposed to AAI? No study has ever considered employment and price effects simultaneously for both offshoring and AAI on attitudes towards these shocks. This paper investigates the political consequences of automation and artificial intelligence (AAI) and offshoring by conducting a conjoint experiment in the US and Canada to manipulate information about generative AI and offshoring. We examine the effect of varying information of the costs and benefits of generative AI and offshoring on support for various policy responses. By analyzing the public's reactions to different economic shocks and their perception of trade versus AI, we contribute to a deeper understanding of how economic changes shape political attitudes and policy preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".