I’m going to horribly oversimply this. For example. Say I am wearing a shirt a cheap one for Wal-Mart.

This shirt was produced in a sweat shop. That sweat shop has .0005 deaths per day. Thus by wearing this shirt and supporting the mechanisms that brought it to me. I have a killcount for today a number substantially smaller then .0005 and obviously there’s a tonne of subjectivity on what that number might be.

Now include the dye factory that made the shirt green, the shoes I am wearing, the bus I am riding in, the coffee I drink. All these luxuries and that number may go up a little.

I am wondering if this is somthing that is being considered anywhere is somone building a calculation to determine our daily kill counts.

I’m sure most of us probably don’t what to know what ours might be, but knowing what parts of our daily lives have the highest values we might work harder to change for the better.

  • Zagorath@aussie.zone
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    1 year ago

    You’re not wrong, but how would you measure that?

    Kill counts are at least in principle very easy to count. Work out how many people die, divide that by the number of items produced. That’s your death number.

    Also add in the death number for all the constituent parts. If a shirt contains 10 metres of cotton from a source where cotton is produced at 1 nanodeath per metre, you add on 10 nD to your shirt’s death count.

    Very hard to do in practice (because who’s sharing that data?), but it’s quite simple to do in principle.

    But how do you even begin to put a number on “suffering”?

    • Lemmylefty@vlemmy.net
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      1 year ago

      I can’t speak to what the original poster was imagining, but one option is years of life lost as compared to the average in that country. So if a sweatshop worker lives an average of 64 years of that country’s 68, that’s 4 years of life lost.

      • Seigest@lemmy.caOP
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        1 year ago

        I agree with this. And if a study on this where to include this data thatd be really good.

        But I also thinks that’s too hard to quantify. Even achieving proper data on death caused specifically from labour operations is going to be extreamly tough as the places causing these deaths will deny all accountability.