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Manchester, UK · POP — · EMP 1,280,108 · DATA GRADE B

Manchester, UK

32.3 /100

ILO GenAI exposure index via empirical SOC2020↔ISCO crosswalk · 3-digit data — own scale, not comparable with US/Canada

#53 of 125

more exposed than 58% of UK areas (England & Wales)

Secondary measures

Scenario: if replacement-level AI arrives in 2030

2027 2035

Figure 1. Modeled displacement under the median preset (diffusion k=0.8, ceiling 0.75, automation share 0.45, friction lag 1.5y, attrition 3%/y). Solid: positions eliminated. The gap between gross and layoffs is natural attrition — speed of diffusion, not depth of exposure, determines layoffs. This is a scenario, not a forecast: adjust every assumption.

Where the losses land — and your assumptions

Positions eliminated by 2035 per occupation group, under the arrival year selected above. Drag any multiplier if you think we're wrong about a group — your model, your numbers. Multipliers scale that group's task exposure (×0 = immune, ×2 = double).

Table 3. Group exposure = employment-weighted mean task exposure (Eloundou β over the group's local occupations). Bars use the same scenario engine as Figure 1 (median preset).

Most exposed local occupations

OccupationJobsMedian wageExposure [range]
Sales Assistants and Retail Cashiers 65,939
37.5
Other Administrative Occupations 31,588
58.9
Sales, Marketing and Related Associate Professionals 34,709
47.3
Information Technology Professionals 30,628
51.2
Functional Managers and Directors 36,103
41.1
Caring Personal Services 67,938
20.6
Administrative Occupations: Finance 25,977
53.6
Customer Service Occupations 27,157
50.3
Teaching and other Educational Professionals 45,089
30.1
Secretarial and Related Occupations 25,204
53.1

Table 2. Ranked by exposure × local employment. Bands on the 0–100 occupation scale.