Does a Stronger Dollar Erode the Profitability of U.S. Firms?
Bibliographic record
Abstract
In 2015, the percentage of S&P 500 sales from foreign countries decreased, compared with 2014 when they ticked up after five years of stagnation.The overall rate for 2015 was 44.3%, down from 47.8% in 2014 and up from the rate of 46% seen in each of the prior five years (2009)(2010)(2011)(2012)(2013).S&P 500 foreign sales represent products and services produced and sold outside of the U.S. European sales continued to increase in 2015, with Europe becoming the dominant region and accounting for 7.79% of all S&P 500 sales, up from the prior year's 7.46%, which was up from 2013's 6.80% rate.After declining four years in a row, sales in the U.K. increased to 1.86% from 0.89% in 2014, 1.12% in 2013, 1.73% in 2012, and 2.39% in 2011. Asian sales reversed their course and decreased, representing 6.77% of S&P 500 sales, down from 7.80% in 2014 and 7.71% in 2013.Canadian sales decreased to 1.17% from the prior year's rate of 3.51%, as declines were seen in oil and commodity prices, and demand for related services and equipment fell. African declared sales decreased to 3.16% from the 4.09% calculated for 2014 and 3.55% for 2013. Energy took the title of leader in exposure to foreign sales, as its domestic sales fell.The sector reported 57.88% of its sales as foreign, up from 2014's 56.23%.Information technology's exposure declined to 57.78% (a tick below energy's) from its rate of 59.39% in 2014.In terms of its sector-level representation of total sales, however, information technology represented 21.93% of all foreign sales, up from 18.34% in 2014, as energy represented 15.46%, down from 21.54% in 2014.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".