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Elementary occupations · code 9624

Water and firewood collectors

Water and firewood collectors collect water and firewood and transport them on foot or using hand or animal carts. Tasks include - (a) cutting and collecting wood from forests for sale in market or as fuel or for own consumption; (b) visiting forests or fields to pick pieces of dried wood from the ground and arranging them in heaps; (c) cutting decayed branches and trunks of trees using axes and hand-saws; (d) tying collected wood into small faggots and carrying them or transporting them on a cart to the market for sale or to villages or households for use; (e) drawing water from wells, rivers or ponds, etc. for domestic use; (f) collecting water in leather bags, buckets or other containers from taps, rivers, ponds or wells, and delivering the water to work sites, the houses of clients or to own household for drinking, cleaning of drains or storage in tanks. Examples of the occupations classified here: - Firewood collector - Water collector

None of this job's tasks scored high enough to count. It ranks 3 of 427 for holding up against AI.

91.1
Holds up
against AI Not Exposed

AI could take on 8.9 of 100, on average
so it holds up at 100 − 8.9 = 91.1

That's the whole sum, and you can check it against the ILO's study.

65
How hands-on
out of 100

From O*NET, averaged across 2 US jobs

45.7
How fiddly
and cramped

finger skill 42.9 · hand skill 45.6 · cramped spaces 48.6 · averaged

Kind of hands-on work · hands-on 50+ · fiddly 39.4+ Hands-on, fiddly work in tight spaces

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.7-work-setting measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this job rates 65 out of 100 for how hands-on it is, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 15, while the average is 8.9. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  1. 15

    Collecting water in leather bags, buckets or other containers from taps, rivers, ponds or wells, and delivering the water to work sites, the houses of clients or to own household for drinking, cleaning of drains or storage in tanks.

    Very Low
  2. 9

    Cutting and collecting wood from forests for sale in market or as fuel or for own consumption

    Very Low
  3. 8

    Drawing water from wells, rivers or ponds, etc. for domestic use

    Very Low
  4. 7.3

    Cutting decayed branches and trunks of trees using axes and hand-saws

    Very Low
  5. 7.3

    Tying collected wood into small faggots and carrying them or transporting them on a cart to the market for sale or to village or household for use

    Very Low
  6. 7

    Visiting forests or fields to pick pieces of dried wood from ground and arranging them in heaps

    Very Low

6 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.7-work-setting

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 84.8. This one is above that.