Between 2000 and 2007, Autor, Dorn, and Hanson (AER, 2013) estimated the change in Chinese imports per worker (in thousands of USD) to be 2.95 for Pittsburgh, PA, and 0.70 for New Orleans, LA. If the corresponding annual change in manufacturing employment per working-age population (in %) was -5% for New Orleans during this period, which of the following changes is likely true for Pittsburgh, PA?

Question

BlackTom AI · Accepted Answer

Greater import exposure is associated with larger manufacturing employment declines, so use proportional scaling of the reported import-per-worker changes to infer the employment impact.

1. Relevant knowledge points and concepts
- Autor, Dorn, and Hanson estimate how increased import exposure (Chinese imports per worker) correlates with declines in manufacturing employment per working-age population.
- If two cities differ in import exposure, and the employment response is roughly proportional to exposure, you can scale the known employment change by the ratio of exposures.
- This uses a linear-proportionality assumption (common for back-of-the-envelope comparisons using estimated effects).

2. Complete reasoning process step by step
- New Orleans: import change = 0.70 (thousand USD per worker) → annual manufacturing employment change = −5%.
- Pittsburgh: import change = 2.95 (thousand USD per worker). Assume employment change scales with import change.
- Compute the exposure ratio: Pittsburgh exposure / New Orleans exposure = 2.95 / 0.70.

3. Calculations with intermediate steps
- Ratio = 2.95 ÷ 0.70 = 4.2142857 (approximately).
- Scale the New Orleans employment change: −5% × 4.2142857 = −21.0714285% (approximately).
- Rounded to the nearest option available, this is about −20%.

4. Why the correct answer is correct
- The computed proportional impact for Pittsburgh is roughly −21.07% per year; among the choices provided, −20% is the closest and therefore the correct selection under the proportionality assumption.

5. Why other options are incorrect
- −3%: far too small in magnitude compared with the scaled estimate (should be ~−21%).
- 5%: wrong sign and magnitude; positive growth contradicts the negative effect associated with higher imports.
- “Insufficient information”: while full causal precision requires regression output, the question gives enough data to make a proportional assessment, so this option is not appropriate.

6. Conclusion
- The proportional calculation yields an approximate annual change of −21.07%, so the correct choice among the options is −20%, matching the provided answer.

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