CLANKED

Epping Forest, UK · POP — · EMP 65,800 · DATA GRADE B

Epping Forest, UK

34.5 /100

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

#7 of 125

more exposed than 95% 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]
Functional Managers and Directors 3,863
41.1
Secretarial and Related Occupations 2,526
53.1
Other Administrative Occupations 2,024
58.9
Sales, Marketing and Related Associate Professionals 2,015
47.3
Sales Assistants and Retail Cashiers 2,297
37.5
Administrative Occupations: Finance 1,600
53.6
Managers and Proprietors in Other Services 2,461
32.7
Production Managers and Directors 2,188
36
Information Technology Professionals 1,498
51.2
Finance Professionals 1,422
53.4

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