Gender and Skills Impact of Tariff Threats: Case Study of Tariff Announcements by the United States
Bibliographic record
Abstract
Using recent United States (US) tariff announcements as a backdrop, we analysed the impact of an illustrative increase in tariffs by the US on key industries under several scenarios, representing either reciprocal tariffs or no reciprocal tariffs by trading partners, and tariffs on a narrow range of countries (Canada, Mexico and China) or tariffs on all countries. Our analysis is focussed on the potential gender and skills impacts of the tariff scenarios, and potential implications for the agribusiness sector where women play a notable role. We used the QGEM Computable General Equilibrium model, which is a global economy-wide modelling framework based on the Global Trade Analysis Project database. The modelling framework is further enhanced by using International Labour Organisation’s labour data in combination with Australian Bureau of Statistics data as a novel addition to the database and the analysis. Using these data sources we have developed a database that separately specifies ten labour market cohorts, considering women and men across five skill categories. To test the potential range of impacts, we undertook modelling of four alternate tariff specifications, in combination with two alternate labour market specifications. Our findings indicate that, in a direct sense, men are globally overrepresented in the most trade- exposed sectors when compared to the aggregate global workforce. In stark contrast, our findings are that the labour force impacts of a range of trade scenarios fall notably disproportionately on the shoulders of women. China, the US and Canada have a relatively high representation of women in aggregate compared to the global average. Trade policies that disproportionately impact these economies relative to regions with lower female representation (e.g. Rest of South Asia and Rest of Asia regions) result in a disproportionately high level of female job losses globally. Our agribusiness sector analysis highlights that trade flows of grains and plant-based products, and livestock and animal products, between the US and other countries are likely to be adversely affected by the US tariffs and also by the reciprocal tariffs by other countries. This will also contribute to the adverse implications for the low and unskilled women in the labour force of the agricultural sectors in many countries. Our analysis indicates a mixed outcome for Australia. For Australia, there is a marginal increase in employment for all worker categories under most tariff scenarios. A key driver of this result is an increase in Australian exports to the US (as with those from all other excluded countries) in response to changes in relative prices, driving a modest increase in employment in export sectors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".