The Impact of Globalization and Digitalization on the Phillips Curve
Bibliographic record
Abstract
COVID-19 has affected globalization and digitalization in opposing ways. Globalization—a process of increasingly stronger integration with the world economy where firms can easily access foreign capital and sell a considerable share of their production abroad—has stalled. The global response to the pandemic and the associated collapse in demand have led to a significant drop in trade and financial linkages between countries. Digitalization—firms’ greater use of digital technologies, such as digitizing information and automating manual tasks—has increased. In particular, the physical distancing requirements related to COVID-19 have contributed to more online shopping, remote work and use of industrial robots. We do not cover the developments of the COVID-19 period itself. We examine the impact of globalization and digitalization on the Phillips curve, which represents the sensitivity of inflation to economic activity. We use a sample of 18 advanced economies over two decades and industry-level data with a cross-country dimension. We first estimate Phillips curves for each decade by relating the growth rate of output prices to past inflation and an employment gap. We then assess the relative impact of globalization and digitalization on these Phillips curves. We measure globalization by increases in trade and financial integration and digitalization by the use of industrial robots as a share of a country’s population. We find that globalization significantly reduces the sensitivity of inflation to domestic economic activity, while digitalization has the opposite effect. Some evidence shows that globalization decreases the level of inflation and digitalization increases it. Evidence for the impact of both trends on employment is not as conclusive. We find that the negative impact of globalization is less in industries with high growth in productivity and that the positive impact of digitalization is less in industries with high past investments in information technology.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".