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County Durham, UK · POP — · EMP 222,216 · DATA GRADE B

County Durham, UK

30.7 /100

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

#103 of 125

more exposed than 18% 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 11,907
37.5
Other Administrative Occupations 5,272
58.9
Caring Personal Services 13,945
20.6
Teaching and other Educational Professionals 8,646
30.1
Customer Service Occupations 4,411
50.3
Administrative Occupations: Government and Related Organisations 4,090
52.1
Secretarial and Related Occupations 3,985
53.1
Road Transport Drivers 8,169
24.3
Sales, Marketing and Related Associate Professionals 4,167
47.3
Administrative Occupations: Finance 3,639
53.6

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