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How to Test Labour Time?


What follows is a brain fart I had this evening...

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How will anyone ever test labour time to see whether or not there is a robust relationship between labour time invested in a good and the sale price of that good? Obviously no labour theory advocate is going to point to one good, on one day, and say that the one price on that day maps to the labour time that went into it. Let's not let our urge to straw man people we disagree with overtake us entirely.

No. What they will say is that changes in prices will map to changes in labour time. Those changes over longer timeframes will be used to estimate with whatever level of precision the relationship between the socially necessary abstract labour time and the market price.

Folks who advocate labour theories don't consider them to operate in a vacuum anymore. If any Neo Ricardian sees this they will probably wonder why I'm saying "anymore" as they know better than me what Smith and Ricardo were into. But objective value theories are hella old, with Aristotle positing something resembling a confused cost/labour theory. Let's put together an evil experiment!

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Imagine a study in which the hypothesis is that number of workers and duration of time to produce do not correlate strongly with the sale price of a given good. The intrepid economist would pick a particular good and then find the total amount of time and total number of people involved in producing it and getting it to market.

Calculation will be problematic, and several different methods will have to be used side by side;

No. of workers (n) + length of process in hours/years (hr) + money price in currency units ($)

T1T2T3T4
                                                                                     
FIRM An1 p1 | $n1 p1 | $n1 p1 | $n1 p1 | $
 n1 p2 | $n1 p2 | $n1 p2 | $n1 p2 | $
 n1 p3 | $n1 p3 | $n1 p3 | $n1 p3 | $
 n1 p4 | $n1 p4 | $n1 p4 | $n1 p4 | $
     
FIRM Bn2 p1 | $n2 p1 | $n2 p1 | $n2 p1 | $
 n2 p2 | $n2 p2 | $n2 p2 | $n2 p2 | $
 n2 p3 | $n2 p3 | $n2 p3 | $n2 p3 | $
 n2 p4 | $n2 p4 | $n2 p4 | $n2 p4 | $
     
FIRM Cn3 p1 | $n3 p1 | $n3 p1 | $n3 p1 | $
 n3 p2 | $n3 p2 | $n3 p2 | $n3 p2 | $
 n3 p3 | $n3 p3 | $n3 p3 | $n3 p3 | $
 n3 p4 | $n3 p4 | $n3 p4 | $n3 p4 | $
     
FIRM Dn4 p1 | $n4 p1 | $n4 p1 | $n4 p1 | $
 n4 p2 | $n4 p2 | $n4 p2 | $n4 p2 | $
 n4 p3 | $n4 p3 | $n4 p3 | $n4 p3 | $
 n4 p4 | $n4 p4 | $n4 p4 | $n4 p4 | $


So what will the relationship be between, say,  n1p1|$n1p1 and n3p1|$n3p1 eh? If I were to collect the 64 sets of worker numbers, production/distribution times and prices what trends would I get? Bear in mind the good is constant across all 64 sets and each T value is a specific start time - as close as possible the same start time for all four firms.

I guess I'll have to return to this matter and find a good that is easy to measure. Of course there are problems like how to measure a process that will take multiple days or weeks. There's also the matter of . Then there's

However I highly doubt that any robust relationship will emerge from this inquiry. Yes that is a bias, and it could be proved wrong when I get around to testing this hypothesis. What about on the macro scale?

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The idea that labour time correlates neatly with prices, even at the macro level, is obviously bullshit if capital counts as stored labour because as capital intensity goes up the aggregate quantity of various labour times going into the capital used in the production of the good will go up overall as the labour time involved in making the good itself goes down, so that the labour is simply being spread across more and more stages in the production structure.

This is why there's still plenty of work to do even with fancy productive machines and 7 billion of us traipsing around, and shoots down any neat line between labour time and falling prices over long time frames, as prices fall while the aggregate labour figure remains roughly constant. Note that if you take issue with me for the previous sentence you're going to have to shelve any idea of macroeconomic value theories right now.

Without long time scales macroeconomics of any sort is useless for describing reality, and even with long timeframes you only ever get very broad brush strokes, like the rise in global GDP per capita/median income/life expectancy etc...

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